Temperature-resilient solid-state organic artificial synapses for neuromorphic computing

Bioinspired Electrolyte-Gated Organic Synaptic Transistors: From Fundamental Requirements to Applications

Rapid development of artificial intelligence requires the implementation of hardware systems with bioinspired parallel information processing and presentation and energy efficiency. Electrolyte-gated organic transistors (EGOTs) offer significant advantages as neuromorphic devices due to their ultra-low operation voltages, minimal hardwired connectivity, and similar operation environment as electrophysiology. Meanwhile, ionic-electronic coupling and the relatively low elastic moduli of organic channel materials make EGOTs suitable for interfacing with biology. This review presents an overview of the device architectures based on organic electrochemical transistors and organic field-effect transistors. Furthermore, we review the requirements of low energy consumption and tunable synaptic plasticity of EGOTs in emulating biological synapses and how they are affected by the organic materials, electrolyte, architecture, and operation mechanism. In addition, we summarize the basic operation principle of biological sensory systems and the recent progress of EGOTs as a building block in artificial systems. Finally, the current challenges and future development of the organic neuromorphic devices are discussed.

Gel-Based Electrolytes for Organic Electrochemical Transistors: Mechanisms, Applications, and Perspectives

Organic electrochemical transistors (OECTs) have emerged as the core component of specialized bioelectronic technologies due to their high signal amplification capability, low operating voltage (<1 V), and biocompatibility. Under a gate bias, OECTs modulate device operation via ionic drift between the electrolyte and the channel. Compared to common electrolytes with a fluid nature (including salt aqueous solutions and ion liquids), gel electrolytes, with an intriguing structure consisting of a physically and/or chemically crosslinked polymer network where the interstitial spaces between polymers are filled with liquid electrolytes or mobile ion species, are promising candidates for quasi-solid electrolytes. Due to relatively high ionic conductivity, the potential for large-scale integration, and the capability to suppress channel swelling, gel electrolytes have been a research highlight in OECTs in recent years. This review summarizes recent progress on OECTs with gel electrolytes that demonstrate good mechanical as well as physical and chemical stabilities. Moreover, various components in forming gel electrolytes, including different mobile liquid phases and polymer components, are introduced. Furthermore, applications of these OECTs in the areas of sensors, neuromorphics, and organic circuits, are discussed. Last, future perspectives of OECTs based on gel electrolytes are discussed along with possible solutions for existing challenges.

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. In this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing

Neuromorphic hardware facilitates rapid and energy-efficient training and operation of neural network models for artificial intelligence. However, existing analog in-memory computing devices, like memristors, continue to face significant challenges that impede their commercialization. These challenges include high variability due to their stochastic nature. Microfabricated electrochemical synapses offer a promising approach by functioning as an analog programmable resistor based on deterministic ion-insertion mechanisms. Here, an all-solid-state oxide-ion synaptic transistor is developed, employing Bi2V0.9Cu0.1O5.35 as a superior oxide-ion conductor electrolyte and La0.5Sr0.5FeO3-δ as a variable-resistance channel able to efficiently operate at temperatures compatible with conventional electronics. This transistor exhibits essential synaptic behaviors such as long- and short-term potentiation, paired-pulse facilitation, and post-tetanic potentiation, mimicking fundamental properties of biological neural networks. Key criteria for efficient neuromorphic computing are satisfied, including excellent linear and symmetric synaptic plasticity, low energy consumption per programming pulse, and high endurance with minimal cycle-to-cycle variation. Integrated into an artificial neural network (ANN) simulation for handwritten digit recognition, the presented synaptic transistor achieved a 96% accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, illustrating the effective implementation of the device in ANNs. These findings demonstrate the potential of oxide-ion based synaptic transistors for effective implementation in analog neuromorphic computing based on iontronics.

Ultrafast Lithium-Ion Transport Engineered by Nanoconfinement Effect

Amid the burgeoning demand for electrochemical energy storage and neuromorphic computing, fast ion transport behavior has attracted widespread attention at both fundamental and practical levels. Here, based on the nanoconfined channel of graphene oxide laminar membranes (GOLMs), the lithium ionic conductivity typically exceeding 102 mS cm-1 is realized, one to three orders of magnitude higher than traditional liquid or solid lithium-ion electrolyte. Specifically, the nanoconfined lithium hexafluorophosphate (LiPF6)-ethylene carbonate (EC)/ dimethyl carbonate (DMC) electrolyte demonstrates the ionic conductivity of 170 mS cm-1, outperforming the bulk counterpart by ≈16 fold. At the ultralow temperature of -60 °C, the nanoconfined electrolyte also maintains a practically useful conductivity of 11 mS cm-1. Furthermore, the in situ experimental and theoretical framework enables to attribute the enhanced ionic conductivity to the layer-by-layer cations and anions distribution induced by high surface charge and nanoconfinement effects in GO nanochannels. More importantly, integrating such rapid lithium-ion transport nanochannel into the LiFePO4 (LFP) cathode significantly improves the high-rate and long-cycle performance of lithium batteries. These results exhibit the convention-breaking ionic conductivity of nanoconfined electrolytes, inspiring the development of ultrafast ion diffusion pathways based on 2D nanoconfined channels for efficient energy storage applications.

Kinetics of Volatile and Nonvolatile Halide Perovskite Devices: The Conductance-Activated Quasi-Linear Memristor (CALM) Model

Memristors stand out as promising components in the landscape of memory and computing. Memristors are generally defined by a conductance mechanism containing a state variable that imparts a memory effect. The current-voltage cycling causes transitions of conductance, which are determined by different physical mechanisms, such as the formation of conducting filaments in an insulating surrounding. Here, we provide a unified description of the set and reset processes using a conductance-activated quasi-linear memristor (CALM) model with a unique voltage-dependent relaxation time of the memory variable. We focus on halide perovskite memristors and their intersection with neuroscience-inspired computing. We show that the modeling approach adeptly replicates the experimental traits of both volatile and nonvolatile memristors. Its versatility extends across various device materials and configurations, as W/SiGe/a-Si/Ag, Si/SiO2/Ag, and SrRuO3/Cr-SrZrO3/Au memristors, capturing nuanced behaviors such as scan rate and upper vertex dependence. The model also describes the response to sequences of voltage pulses that cause synaptic potentiation effects. This model is a potent tool for comprehending and probing the dynamical response of memristors by indicating the relaxation properties that control observable responses.

Trivalent Ionic Molecular Bridges as Efficient Charge-Trapping Method for All-Solid-State Organic Synaptic Transistors toward Neuromorphic Signal Processing Applications

Achieving high retention of memory state is crucial in artificial synapse devices for neuromorphic computing systems. Of various memorizing methods, a charge-trapping method provides fast response times when it comes to the smallest size of electrons. Here, for the first time, it is demonstrated that trivalent molecular bridges with three ionic bond sites in the polymeric films can efficiently trap electrons in the organic synaptic transistors (OSTRs). A water-soluble polymer with sulfonic acid groups, poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA), is reacted with melamine (ML) to make trivalent molecular bridges with three ionic bond sites for the application of charge-trapping and gate-insulating layer in all-solid-state OSTRs. The OSTRs with the PAMPSA:ML layers are operated at low voltages (≤5 V) with pronounced hysteresis and high memory retention characteristics (ML = 25 mol%) and delivered excellent potentiation/depression performances under modulation of gate pulse frequency. The optimized OSTRs could successfully process analog (Morse/Braile) signals to synaptic current datasets for recognition/prediction logics with an accuracy of >95%, supporting strong potential as all-solid-state synaptic devices for neuromorphic systems in artificial intelligence applications.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Ionic Liquid Gated Organic Electrochemical Transistors with Broadened Bandwidth

The organic electrochemical transistor (OECT) is a biosignal transducer known for its high amplification but relatively slow operation. Here, we demonstrate that the use of an ionic liquid as the dielectric medium significantly improves the switching speed of a p-type enhancement-mode OECT, regardless of the gate electrode used. The OECT response time with the ionic liquid improves up to ca. 41-fold and 46-fold for the silver/silver chloride (Ag/AgCl) and gold (Au) gates, respectively, compared with devices gated with the phosphate buffered saline (PBS) solution. Notably, the transistor gain remains uncompromised, and its maximum is reached at lower voltages compared to those of PBS-gated devices with Ag/AgCl as the gate electrode. Through ultraviolet-visible spectroscopy and etching X-ray photoelectron spectroscopy characterizations, we reveal that the enhanced bandwidth is associated with the prediffused ionic liquid inside the polymer, leading to a higher doping level compared to PBS. Using the ionic liquid-gated OECTs, we successfully detect electrocardiography (ECG) signals, which exhibit a complete waveform with well-distinguished features and a stable signal baseline. By integrating nonaqueous electrolytes that enhance the device bandwidth, we unlock the potential of enhancement-mode OECTs for physiological signal acquisition and other real-time biosignal monitoring applications.

Advanced Applications of Polymer Hydrogels in Electronics and Signal Processing

The current state of affairs in the field of using polymer hydrogels for the creation of innovative systems for signal and image processing, of which computing is a special case, is analyzed. Both of these specific examples of systems capable of forming an alternative to the existing semiconductor-based computing technology, but assuming preservation of the used algorithmic basis, and non-trivial signal converters, the nature of which requires transition to fundamentally different algorithms of data processing, are considered. It is shown that the variability of currently developed information processing systems based on the use of polymers, including polymer hydrogels, leads to the need to search for complementary algorithms. Moreover, the well-known thesis that modern polymer science allows for the realization of functional materials with predetermined properties, at the present stage, receives a new sounding: it is acceptable to raise the question of creating systems built on a quasi-biological basis and realizing predetermined algorithms of information or image processing. Specific examples that meet this thesis are considered, in particular, promising information protection systems for UAV groups, as well as systems based on the coupling of neural networks with holograms that solve various applied problems. These and other case studies demonstrate the importance of interdisciplinary cooperation for solving problems arising from the need for further modernization of signal processing systems.

Controlling Long-Term Plasticity in Neuromorphic Computing Through Modulation of Ferroelectric Polarization

Electrolyte-gated transistors (EGTs) have significant potential for neuromorphic computing because they can control the number of ions by mimicking neurotransmitters. However, fast depolarization of the electric double layer (EDL) makes it difficult to achieve long-term plasticity (LTP). Additionally, most research utilizing organic ferroelectric materials has been focused on basic biological functions, and the impact on nonvolatile memory properties is still lacking. Herein, we present a polyvinylidene fluoride (PVDF)-based ion-gel synaptic device using PVDF and poly(vinylidene fluoride-co-hexafluoropropylene) (P(VDF-HFP)) to implement LTP through the introduction of ferroelectric materials. The PVDF-based polymer slows the escape rate of TFSI anions from the electrolyte/channel layer through residual polarization. The fabricated synaptic devices successfully demonstrate LTP by controlling ion adsorption under the influence of PVDF-based polymers. Furthermore, it implements synaptic functions including paired pulse facilitation (PPF), high-pass filtering, and neurotransmitter control. To validate the potential of neuromorphic computing, we successfully achieved high recognition rates for artificial/convolutional neural network (A/CNN) simulations via sequential adsorption and desorption under ferroelectric polarization with long-term potentiation/depression (LTP/D). This study provides a rational ion adsorption strategy utilizing the ferroelectric polarization caused by the introduction of a PVDF-based polymer in the dielectric layer.

Increasing the stability of electrolyte-gated organic synaptic transistors for neuromorphic implants

Electrolyte-gated organic synaptic transistors (EGOSTs) can have versatile synaptic plasticity in a single device, so they are promising as components of neuromorphic implants that are intended for use in neuroprosthetic electronic nerves that are energy-efficient and have simple system structure. With the advancement in transistor properties of EGOSTs, the commercialization of neuromorphic implants for practical long-term use requires consistent operation, so they must be stable in vivo. This requirement demands strategies that maintain electronic and ionic transport in the devices while implanted in the human body, and that are mechanically, environmentally, and operationally stable. Here, we cover the structure, working mechanisms, and electrical responses of EGOSTs. We then focus on strategies to ensure their stability to maintain these characteristics and prevent adverse effects on biological tissues. We also highlight state-of-the-art neuromorphic implants that incorporate these strategies. We conclude by presenting a perspective on improvements that are needed in EGOSTs to develop practical, neuromorphic implants that are long-term useable.

High-Yield Production of Solution-Processed Highly Robust Organic Artificial Synapses by Thermal Treatments

A promising approach for implementing biomimetic systems relies on organic electronic devices designed to emulate neural synapses. However, organic artificial synapses face challenges in achieving high yield and robustness, rendering them difficult to use in practical applications. In this work, a high-yield and highly stable bulk heterojunction (BHJ) synaptic device composed of Poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) was fabricated via a simple solution process followed by thermal treatments. The crystallinity of P3HT and the precipitation of PCBM in BHJ films can be controlled by the thermal annealing temperatures. At 80 °C, P3HT reaches its highest crystallinity, while PCBM remains uniformly distributed. This thermal treatment significantly contributes to the fabrication of devices characterized by a high yield rate, reaching 98.43%. Additionally, this device remained operational even after being immersed in deionized water, ethanol, and seawater for 100 h. More importantly, it exhibited high elasticity over a wide temperature range from -90 to 310 °C. Finally, this device was utilized to construct a biomimetic vehicle with autonomous memory learning capabilities. After repeated training, the avoidance time was optimized by 31.4%. The robust P3HT:PCBM artificial synapses hold great promise for advancing the development of biomimetic electronic products in extreme environments.

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot

The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (-0.6 V) achieved by integrating a pressure-activated organic electrochemical synaptic transistor and artificial mechanoreceptors. The dendritic integration function for neurorobotics is achieved to perceive directional movement of object, further reducing the control complexity by exploiting the distributed and parallel networks. An intelligent robot assembled with artificial afferent nerve, coupled with a closed-loop feedback program is demonstrated to rapidly implement slip recognition and prevention actions upon occurrence of object slippage. The spatiotemporal features of tactile patterns are well differentiated with a high recognition accuracy after processing spike-encoded signals with deep learning model. This work represents a breakthrough in mimicking synaptic behaviors, which is essential for next-generation intelligent neurorobotics and low-power biomimetic electronics.

Bistable organic electrochemical transistors: enthalpy vs. entropy

Organic electrochemical transistors (OECTs) underpin a range of emerging technologies, from bioelectronics to neuromorphic computing, owing to their unique coupling of electronic and ionic charge carriers. In this context, various OECT systems exhibit significant hysteresis in their transfer curve, which is frequently leveraged to achieve non-volatility. Meanwhile, a general understanding of its physical origin is missing. Here, we introduce a thermodynamic framework that readily explains the emergence of bistable OECT operation via the interplay of enthalpy and entropy. We validate this model through temperature-resolved characterizations, material manipulation, and thermal imaging. Further, we reveal deviations from Boltzmann statistics for the subthreshold swing and reinterpret existing literature. Capitalizing on these findings, we finally demonstrate a single-OECT Schmitt trigger, thus compacting a multi-component circuit into a single device. These insights provide a fundamental advance for OECT physics and its application in non-conventional computing, where symmetry-breaking phenomena are pivotal to unlock new paradigms of information processing.

Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks

Neural network training can be slow and energy-expensive due to the frequent transfer of weight data between digital memory and processing units. Neuromorphic systems can accelerate neural networks by performing multiply-accumulate operations in parallel using nonvolatile analog memory. However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information-and therefore storage-of the partial derivatives of the weight values preventing suitable and scalable implementation in hardware. Here, we propose a hardware implementation of the backpropagation algorithm that progressively updates each layer using in situ stochastic gradient descent, avoiding this storage requirement. We experimentally demonstrate the in situ error calculation and the proposed progressive backpropagation method in a multilayer hardware-implemented neural network. We confirm identical learning characteristics and classification performance compared to conventional backpropagation in software. We show that our approach can be scaled to large and deep neural networks, enabling highly efficient training of advanced artificial intelligence computing systems.

Transient Response and Ionic Dynamics in Organic Electrochemical Transistors

The rapid development of organic electrochemical transistors (OECTs) has ushered in a new era in organic electronics, distinguishing itself through its application in a variety of domains, from high-speed logic circuits to sensitive biosensors, and neuromorphic devices like artificial synapses and organic electrochemical random-access memories. Despite recent strides in enhancing OECT performance, driven by the demand for superior transient response capabilities, a comprehensive understanding of the complex interplay between charge and ion transport, alongside electron-ion interactions, as well as the optimization strategies, remains elusive. This review aims to bridge this gap by providing a systematic overview on the fundamental working principles of OECT transient responses, emphasizing advancements in device physics and optimization approaches. We review the critical aspect of transient ion dynamics in both volatile and non-volatile applications, as well as the impact of materials, morphology, device structure strategies on optimizing transient responses. This paper not only offers a detailed overview of the current state of the art, but also identifies promising avenues for future research, aiming to drive future performance advancements in diversified applications.

Organic Mixed Ionic-Electronic Conductors for Bioelectronic Sensors: Materials and Operation Mechanisms

The field of organic mixed ionic-electronic conductors (OMIECs) has gained significant attention due to their ability to transport both electrons and ions, making them promising candidates for various applications. Initially focused on inorganic materials, the exploration of mixed conduction has expanded to organic materials, especially polymers, owing to their advantages such as solution processability, flexibility, and property tunability. OMIECs, particularly in the form of polymers, possess both electronic and ionic transport functionalities. This review provides an overview of OMIECs in various aspects covering mechanisms of charge transport including electronic transport, ionic transport, and ionic-electronic coupling, as well as conducting/semiconducting conjugated polymers and their applications in organic bioelectronics, including (multi)sensors, neuromorphic devices, and electrochromic devices. OMIECs show promise in organic bioelectronics due to their compatibility with biological systems and the ability to modulate electronic conduction and ionic transport, resembling the principles of biological systems. Organic electrochemical transistors (OECTs) based on OMIECs offer significant potential for bioelectronic applications, responding to external stimuli through modulation of ionic transport. An in-depth review of recent research achievements in organic bioelectronic applications using OMIECs, categorized based on physical and chemical stimuli as well as neuromorphic devices and circuit applications, is presented.

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.

All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing

Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1 mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.

Strengthening Multi-Factor Authentication Through Physically Unclonable Functions in PVDF-HFP-Phase-Dependent a-IGZO Thin-Film Transistors

For enhanced security in hardware-based security devices, it is essential to extract various independent characteristics from a single device to generate multiple keys based on specific values. Additionally, the secure destruction of authentication information is crucial for the integrity of the data. Doped amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) using poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) induce a dipole doping effect through a phase-transition process, creating physically unclonable function (PUF) devices for secure user information protection. The PUF security key, generated at VGS = 20 V in a 20 × 10 grid, demonstrates uniformity of 42% and inter-Hamming distance (inter-HD) of 49.79% in the β-phase of PVDF-HFP. However, in the γ-phase, the uniformity drops to 22.5%, and inter-HD decreases to 35.74%, indicating potential security key destruction during the phase transition. To enhance security, a multi-factor authentication (MFA) system is integrated, utilizing five security keys extracted from various TFT parameters. The security keys from turn-on voltage (VON), VGS = 20 V, VGS = 30 V, mobility, and threshold voltage (Vth) exhibit near-ideal uniformities and inter-HDs, with the highest values of 58% and 51.68%, respectively. The dual security system, combining phase transition and MFA, establishes a robust protection mechanism for privacy-sensitive user information.

Tuning Gate Potential Profiles and Current-Voltage Characteristics of Polymer Electrolyte-Gated Transistors by Capacitance Engineering

We demonstrate that the transfer characteristics of electrolyte-gated transistors (EGTs) with polythiophene semiconductor channels are a strong function of gate/electrolyte interfacial contact area, i.e., gate size. Polythiophene EGTs with gate/electrolyte areas much larger than the channel/electrolyte areas show a clear peak in the drain current vs gate voltage (ID-VG) behavior, as well as peak voltage hysteresis between the forward and reverse VG sweeps. Polythiophene EGTs with small gate/electrolyte areas, on the other hand, exhibit current plateaus in the ID-VG behavior and a gate-size-dependent hysteresis loop between turn on and off. The qualitatively different transport behaviors are attributed to the relative sizes of the gate/electrolyte and channel/electrolyte interface capacitances, which are proportional to interfacial area. These interfacial capacitances are in series with each other such that the total capacitance of the full gate/electrolyte/channel stack is dominated by the interface with the smallest capacitance or area. For EGTs with large gates, most of the applied VG is dropped at the channel/electrolyte interface, leading to very high charge accumulations, up to ∼0.3 holes per ring (hpr) in the case of polythiophene semiconductors. The large charge density results in sub-band-filling and a marked decrease in hole mobility, giving rise to the peak in ID-VG. For EGTs with small gates, hole accumulation saturates near 0.15 hpr, band-filling does not occur, and hole mobility is maintained at a fixed value, which leads to the ID plateau. Potential drops at the interfaces are confirmed by in situ potential measurements inside a gate/electrolyte/polymer semiconductor stack. Hole accumulations are measured with gate current-gate voltage (IG-VG) measurements acquired simultaneously with the ID-VG characteristics. Overall, our measurements demonstrate that remarkably different ID behavior can be obtained for polythiophene EGTs by controlling the magnitude of the gate-electrolyte interfacial capacitance.

The unexpected fast polymerization during the synthesis of a glycolated polythiophene

Conjugated polymers with ethylene glycol side chains are emerging as ideal materials for bioelectronics, particularly for application in organic electrochemical transistors (OECTs). To improve the OECT device performance, it is important to develop an efficient synthetic strategy that will provide access to novel high-performing materials besides focusing on molecular design. While a lot of efforts are being devoted to designing of new polymers by modifying the glycol side chains, understanding how their nature affects the polymerization kinetics and eventually the polymer structure and properties is not known. In this work, we have studied the influence of the content of the ethylene glycol side chain and its linkage on the formation of the active Grignard monomer species upon Grignard metathesis in three thiophene derivatives. A strong dependence of the monomer's concentration on polymerization was noted in our study indicating that for synthesizing P3MEEMT, a high-performing OECT material, by Kumada catalyst transfer polymerization (KCTP) a minimum of 0.15 M monomer is needed. Furthermore, kinetic studies by GPC show uncontrolled polymerization behavior contrary to the controlled chain growth characteristics of the KCTP.

Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

Eutectogels as a Semisolid Electrolyte for Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) are signal transducers offering high amplification, which makes them particularly advantageous for detecting weak biological signals. While OECTs typically operate with aqueous electrolytes, those employing solid-like gels as the dielectric layer can be excellent candidates for constructing wearable electrophysiology probes. Despite their potential, the impact of the gel electrolyte type and composition on the operation of the OECT and the associated device design considerations for optimal performance with a chosen electrolyte have remained ambiguous. In this work, we investigate the influence of three types of gel electrolytes-hydrogels, eutectogels, and iongels, each with varying compositions on the performance of OECTs. Our findings highlight the superiority of the eutectogel electrolyte, which comprises poly(glycerol 1,3-diglycerolate diacrylate) as the polymer matrix and choline chloride in combination with 1,3-propanediol deep eutectic solvent as the ionic component. This eutectogel electrolyte outperforms hydrogel and iongel counterparts of equivalent dimensions, yielding the most favorable transient and steady-state performance for both p-type depletion and p-type/n-type enhancement mode transistors gated with silver/silver chloride (Ag/AgCl). Furthermore, the eutectogel-integrated enhancement mode OECTs exhibit exceptional operational stability, reflected in the absence of signal-to-noise ratio (SNR) variation in the simulated electrocardiogram (ECG) recordings conducted continuously over a period of 5 h, as well as daily measurements spanning 30 days. Eutectogel-based OECTs also exhibit higher ECG signal amplitudes and SNR than their counterparts, utilizing the commercially available hydrogel, which is the most common electrolyte for cutaneous electrodes. These findings underscore the potential of eutectogels as a semisolid electrolyte for OECTs, particularly in applications demanding robust and prolonged physiological signal monitoring.

High-Performance n-Type Polymeric Mixed Ionic-Electronic Conductors: The Impacts of Halogen Functionalization

Developing high-performance n-type polymer mixed ionic-electronic conductors (PMIECs) is a grand challenge, which largely determines their applications in vaious organic electronic devices, such as organic electrochemical transistors (OECTs) and organic thermoelectrics (OTEs). Herein, two halogen-functionalized PMIECs f-BTI2g-TVTF and f-BTI2g-TVTCl built from fused bithiophene imide dimer (f-BTI2) as the acceptor unit and halogenated thienylene-vinylene-thienylene (TVT) as the donor co-unit are reported. Compared to the control polymer f-BTI2g-TVT, the fluorinated f-BTI2g-TVTF shows lower-positioned lowest unoccupied molecular orbital (LUMO), improved charge transport property, and greater ion uptake capacity. Consequently, f-BTI2g-TVTF delivers a state-of-the-art µC* of 90.2 F cm-1 V-1 s-1 with a remarkable electron mobility of 0.41 cm2 V-1 s-1 in OECTs and an excellent power factor of 64.2 µW m-1 K-2 in OTEs. An OECT-based inverter amplifier is further demonstrated with voltage gain up to 148 V V-1 , which is among the highest values for OECT inverters. Such results shed light on the impacts of halogen atoms on developing high-performing n-type PMIECs.

Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing

Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.

Wide-Range Synaptic Current Responses with a Liquid Ga Electrode via a Surface Redox Reaction in a NaOH Solution at Different Molar Concentrations

A liquid Ga-based synaptic device with two-terminal electrodes is demonstrated in NaOH solutions at 50 °C. The proposed electrochemical redox device using the liquid Ga electrode in the NaOH solution can emulate various biological synapses that require different decay constants. The device exhibits a wide range of current decay times from 60 to 320 ms at different NaOH mole concentrations from 0.2 to 1.6 M. This research marks a step forward in the development of flexible and biocompatible neuromorphic devices that can be utilized for a range of applications where different synaptic strengths are required lasting from a few milliseconds to seconds.

Polymer-Based Thermally Stable Chemiresistive Sensor for Real-Time Monitoring of NO2 Gas Emission

We present a thermally stable, mechanically compliant, and sensitive polymer-based NO2 gas sensor design. Interconnected nanoscale morphology driven from spinodal decomposition between conjugated polymers tethered with polar side chains and thermally stable matrix polymers offers judicious design of NO2-sensitive and thermally tolerant thin films. The resulting chemiresitive sensors exhibit stable NO2 sensing even at 170 °C over 6 h. Controlling the density of polar side chains along conjugated polymer backbone enables optimal design for coupling high NO2 sensitivity, selectivity, and thermal stability of polymer sensors. Lastly, thermally stable films are used to implement chemiresistive sensors onto flexible and heat-resistant substrates and demonstrate a reliable gas sensing response even after 500 bending cycles at 170 °C. Such unprecedented sensor performance as well as environmental stability are promising for real-time monitoring of gas emission from vehicles and industrial chemical processes.

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

ECRAM Materials, Devices, Circuits and Architectures: A Perspective

Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.

Electrochemical Ionic Synapses: Progress and Perspectives

Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three-terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid-solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+ , O2- , and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1 V), low energy consumption, with complementary metal-oxide-semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.

Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.

Tunable Intervalence Charge Transfer in Ruthenium Prussian Blue Analog Enables Stable and Efficient Biocompatible Artificial Synapses

Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation state and conductivity in unexplored materials. This report expands the materials playground for neuromorphic devices to include a mixed valence inorganic 3D coordination framework, a ruthenium Prussian blue analog (RuPBA), for flexible and biocompatible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. The electrochemically tunable degree of mixed valency and electronic coupling between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory computations and application of electron transfer theory to in situ spectroscopy of intervalence charge transfer. Retention of programmed states is improved by nearly two orders of magnitude compared to extensively studied organic polymers, thus reducing the frequency, complexity, and energy costs associated with error correction schemes. This report demonstrates dopamine-mediated plasticity of RuPBA synapses and biocompatibility of RuPBA with neuronal cells, evoking prospective application for brain-computer interfacing.

The Influence of Regiochemistry on the Performance of Organic Mixed Ionic and Electronic Conductors

Thiophenes functionalised in the 3-position are ubiquitous building blocks for the design and synthesis of organic semiconductors. Their non-centrosymmetric nature has long been used as a powerful synthetic design tool exemplified by the vastly different properties of regiorandom and regioregular poly(3-hexylthiophene) owing to the repulsive head-to-head interactions between neighbouring side chains in the regiorandom polymer. The renewed interest in highly electron-rich 3-alkoxythiophene based polymers for bioelectronic applications opens up new considerations around the regiochemistry of these systems as both the head-to-tail and head-to-head couplings adopt near-planar conformations due to attractive intramolecular S-O interactions. To understand how this increased flexibility in the molecular design can be used advantageously, we explore in detail the geometrical and electronic effects that influence the optical, electrochemical, structural, and electrical properties of a series of six polythiophene derivatives with varying regiochemistry and comonomer composition. We show how the interplay between conformational disorder, backbone coplanarity and polaron distribution affects the mixed ionic-electronic conduction. Ultimately, we use these findings to identify a new conformationally restricted polythiophene derivative for p-type accumulation-mode organic electrochemical transistor applications with performance on par with state-of-the-art mixed conductors evidenced by a μC* product of 267 F V-1 cm-1 s-1.

The Influence of Regiochemistry on the Performance of Organic Mixed Ionic and Electronic Conductors

Thiophenes functionalised in the 3-position are ubiquitous building blocks for the design and synthesis of organic semiconductors. Their non-centrosymmetric nature has long been used as a powerful synthetic design tool exemplified by the vastly different properties of regiorandom and regioregular poly(3-hexylthiophene) owing to the repulsive head-to-head interactions between neighbouring side chains in the regiorandom polymer. The renewed interest in highly electron-rich 3-alkoxythiophene based polymers for bioelectronic applications opens up new considerations around the regiochemistry of these systems as both the head-to-tail and head-to-head couplings adopt near-planar conformations due to attractive intramolecular S-O interactions. To understand how this increased flexibility in the molecular design can be used advantageously, we explore in detail the geometrical and electronic effects that influence the optical, electrochemical, structural, and electrical properties of a series of six polythiophene derivatives with varying regiochemistry and comonomer composition. We show how the interplay between conformational disorder, backbone coplanarity and polaron distribution affects the mixed ionic-electronic conduction. Ultimately, we use these findings to identify a new conformationally restricted polythiophene derivative for p-type accumulation-mode organic electrochemical transistor applications with performance on par with state-of-the-art mixed conductors evidenced by a μC* product of 267 F V-1  cm-1  s-1 .

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.

Molecularly Hybridized Conduction in DPP-Based Donor-Acceptor Copolymers toward High-Performance Iono-Electronics

Iono-electronics, that is, transducing devices able to translate ionic injection into electrical output, continue to demand a variety of mixed ionic-electronic conductors (MIECs). Though polar sidechains are widely used in designing novel polymer MIECs, it remains unclear to chemists how much balance is needed between the two antagonistic modes of transport (ion permeability and electronic charge transport) to yield high-performance materials. Here, the impact of molecularly hybridizing ion permeability and charge mobility in semiconducting polymers on their performance in electrochemical and synaptic transistors is investigated. A series of diketopyrrolopyrrole (DPP)-based copolymers are employed to demonstrate the multifunctionality attained by controlling the density of polar sidechains along the backbone. Notably, efficient electrochemical signal transduction and reliable synaptic plasticity are demonstrated via controlled ion insertion and retention. The newly designed DPP-based copolymers further demonstrate unprecedented thermal tolerance among organic mixed ionic-electronic conductors, a key property in the manufacturing of organic electronics.

Role of aggregates and microstructure of mixed-ionic-electronic-conductors on charge transport in electrochemical transistors

Synthetic efforts have delivered a library of organic mixed ionic-electronic conductors (OMIECs) with high performance in electrochemical transistors. The most promising materials are redox-active conjugated polymers with hydrophilic side chains that reach high transconductances in aqueous electrolytes due to volumetric electrochemical charging. Current approaches to improve transconductance and device stability focus mostly on materials chemistry including backbone and side chain design. However, other parameters such as the initial microstructure and microstructural rearrangements during electrochemical charging are equally important and are influenced by backbone and side chain chemistry. In this study, we employ a polymer system to investigate the fundamental electrochemical charging mechanisms of OMIECs. We couple in situ electronic charge transport measurements and spectroelectrochemistry with ex situ X-ray scattering electrochemical charging experiments and find that polymer chains planarize during electrochemical charging. Our work shows that the most effective conductivity modulation is related to electrochemical accessibility of well-ordered, interconnected aggregates that host high mobility electronic charge carriers. Electrochemical stress cycling induces microstructural changes, but we find that these aggregates can largely maintain order, providing insights on the structural stability and reversibility of electrochemical charging in these systems. This work shows the importance of material design for creating OMIECs that undergo structural rearrangements to accommodate ions and electronic charge carriers during which percolating networks are formed for efficient electronic charge transport.

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼10³) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

Impact of Large Gate Voltages and Ultrathin Polymer Electrolytes on Carrier Density in Electric-Double-Layer-Gated Two-Dimensional Crystal Transistors

Electric-double-layer (EDL) gating can induce large capacitance densities (∼1-10 μF cm^-2) in two-dimensional (2D) semiconductors; however, several properties of the electrolyte limit performance. One property is the electrochemical activity which limits the gate voltage (V_G) that can be applied and therefore the maximum extent to which carriers can be modulated. A second property is electrolyte thickness, which sets the response speed of the EDL gate and therefore the time scale over which the channel can be doped. Typical thicknesses are on the order of micrometers, but thinner electrolytes (nanometers) are needed for very-large-scale-integration (VLSI) in terms of both physical thickness and the speed that accompanies scaling. In this study, finite element modeling of an EDL-gated field-effect transistor (FET) is used to self-consistently couple ion transport in the electrolyte to carrier transport in the semiconductor, in which density of states, and therefore quantum capacitance, is included. The model reveals that 50 to 65% of the applied potential drops across the semiconductor, leaving 35 to 50% to drop across the two EDLs. Accounting for the potential drop in the channel suggests that higher carrier densities can be achieved at larger applied V_G without concern for inducing electrochemical reactions. This insight is tested experimentally via Hall measurements of graphene FETs for which V_G is extended from ±3 to ±6 V. Doubling the gate voltage increases the sheet carrier density by an additional 2.3 × 10¹³ cm^-2 for electrons and 1.4 × 10¹³ cm^-2 for holes without inducing electrochemistry. To address the need for thickness scaling, the thickness of the solid polymer electrolyte, poly(ethylene oxide) (PEO):CsClO₄, is decreased from 1 μm to 10 nm and used to EDL gate graphene FETs. Sheet carrier density measurements on graphene Hall bars prove that the carrier densities remain constant throughout the measured thickness range (10 nm-1 μm). The results indicate promise for overcoming the physical and electrical limitations to VLSI while taking advantage of the ultrahigh carrier densities induced by EDL gating.

Influence of Backbone Regioregularity on the Optoelectronic and Mechanical Response of Conjugated Polyelectrolyte-Based Hydrogels

The ability to form robust, optoelectronically responsive, and mechanically tunable hydrogels using facile processing is desirable for sensing, biomedical, and light-harvesting applications. We demonstrate that such a hydrogel can be formed using aqueous complexation between one conjugated and one nonconjugated polyelectrolyte. We show that the rheological properties of the hydrogel can be tuned using the regioregularity of the conjugated polyelectrolyte (CPE) backbone, leading to significantly different mesoscale gel morphologies. We also find that the exciton dynamics in the long-time limit reflect differences in the underlying electronic connectivity of the hydrogels as a function CPE regioregularity. The influence of excess small ions on the hydrogel structure and the exciton dynamics similarly depends on the regioregularity in a significant way. Finally, electrical impedance measurements lead us to infer that these hydrogels can act as mixed ionic/electronic conductors. We believe that such gels possess an attractive combination of physical-chemical properties that can be leveraged in multiple applications.

An ordered, self-assembled nanocomposite with efficient electronic and ionic transport

Mixed conductors-materials that can efficiently conduct both ionic and electronic species-are an important class of functional solids. Here we demonstrate an organic nanocomposite that spontaneously forms when mixing an organic semiconductor with an ionic liquid and exhibits efficient room-temperature mixed conduction. We use a polymer known to form a semicrystalline microstructure to template ion intercalation into the side-chain domains of the crystallites, which leaves electronic transport pathways intact. Thus, the resulting material is ordered, exhibiting alternating layers of rigid semiconducting sheets and soft ion-conducting layers. This unique dual-network microstructure leads to a dynamic ionic/electronic nanocomposite with liquid-like ionic transport and highly mobile electronic charges. Using a combination of operando X-ray scattering and in situ spectroscopy, we confirm the ordered structure of the nanocomposite and uncover the mechanisms that give rise to efficient electron transport. These results provide fundamental insights into charge transport in organic semiconductors, as well as suggesting a pathway towards future improvements in these nanocomposites.

Mechanistic and Kinetic Analysis of Perovskite Memristors with Buffer Layers: The Case of a Two-Step Set Process

With the increasing demand for artificially intelligent hardware systems for brain-inspired in-memory and neuromorphic computing, understanding the underlying mechanisms in the resistive switching of memristor devices is of paramount importance. Here, we demonstrate a two-step resistive switching set process involving a complex interplay among mobile halide ions/vacancies (I^-/V_I⁺) and silver ions (Ag⁺) in perovskite-based memristors with thin undoped buffer layers. The resistive switching involves an initial gradual increase in current associated with a drift-related halide migration within the perovskite bulk layer followed by an abrupt resistive switching associated with diffusion of mobile Ag⁺ conductive filamentary formation. Furthermore, we develop a dynamical model that explains the characteristic I-V curve that helps to untangle and quantify the switching regimes consistent with the experimental memristive response. This further insight into the two-step set process provides another degree of freedom in device design for versatile applications with varying levels of complexity.

Large Transconductance of Electrochemical Transistors Based on Fluorinated Donor-Acceptor Conjugated Polymers

Organic electrochemical transistors (OECTs) have enormous potential for use in biosignal amplifiers, analyte sensors, and neuromorphic electronics owing to their exceptionally large transconductance. However, it is challenging to simultaneously achieve high charge carrier mobility and volumetric capacitance, the two most important figures of merit in OECTs. Herein, a method of achieving high-performance OECT with donor-acceptor conjugated copolymers by introducing fluorine units is proposed. A series of cyclopentadithiophene-benzothiadiazole (CDT-BT) copolymers for use in high-performance OECTs with enhanced charge carrier mobility (from 0.65 to 1.73 cm²·V^-1·s^-1) and extended volumetric capacitance (from 44.8 to 57.6 F·cm^-3) by fluorine substitution is achieved. The increase in the volumetric capacitance of the fluorinated polymers is attributed to either an increase in the volume at which ions can enter the film or a decrease in the effective distance between the ions and polymer backbones. The fluorine substitution increases the backbone planarity of the CDT-BT copolymers, enabling more efficient charge carrier transport. The fluorination strategy of this work suggests the more versatile use of conjugated polymers for high-performance OECTs.

Room-temperature valley transistors for low-power neuromorphic computing

Valley pseudospin is an electronic degree of freedom that promises highly efficient information processing applications. However, valley-polarized excitons usually have short pico-second lifetimes, which limits the room-temperature applicability of valleytronic devices. Here, we demonstrate room-temperature valley transistors that operate by generating free carrier valley polarization with a long lifetime. This is achieved by electrostatic manipulation of the non-trivial band topology of the Weyl semiconductor tellurium (Te). We observe valley-polarized diffusion lengths of more than 7 μm and fabricate valley transistors with an ON/OFF ratio of 105 at room temperature. Moreover, we demonstrate an ion insertion/extraction device structure that enables 32 non-volatile memory states with high linearity and symmetry in the Te valley transistor. With ultralow power consumption (~fW valley contribution), we enable the inferring process of artificial neural networks, exhibiting potential for applications in low-power neuromorphic computing.

An Electrochemical-Electret Coupled Organic Synapse with Single-Polarity Driven Reversible Facilitation-to-Depression Switching

Neuromorphic engineering and artificial intelligence demands hardware elements that emulates synapse algorithms. During the last decade electrolyte-gated organic conjugated materials have been explored as a platform for artificial synapses for neuromorphic computing. Unlike biological synapses, in current devices the synaptic facilitation and depression are triggered by voltages with opposite polarity. To enhance the reliability and simplify the operation of the synapse without lowering its sophisticated functionality, here, an electrochemical-electret coupled organic synapse (EECS) possessing a reversible facilitation-to-depression switch, is devised. Electret charging counterbalances channel conductance changes due to electrochemical doping, inducing depression without inverting the gate polarity. Overall, EECS functions as a threshold-controlled synaptic switch ruled by its amplitude-dependent, dual-modal operation, which can well emulate information storage and erase as in real synapses. By varying the energy level offset between the channel material and the electret, the EECS's transition threshold can be adjusted for specific applications, e.g., imparting additional light responsiveness to the device operation. The novel device architecture represents a major step forward in the development of artificial organic synapses with increased functional complexity and it opens new perspectives toward the fabrication of abiotic neural networks with higher reliability, efficiency, and endurance.