Activity Dependent Synaptic Plasticity Mimicked on Indium-Tin-Oxide Electric-Double-Layer Transistor

Biomaterials for neuroengineering: applications and challenges

Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.

Ferroelectric/Electric-Double-Layer-Modulated Synaptic Thin Film Transistors toward an Artificial Tactile Perception System

Tactile sensation and recognition in the human brain are indispensable for interaction between the human body and the surrounding environment. It is quite significant for intelligent robots to simulate human perception and decision-making functions in a more human-like way to perform complex tasks. A combination of tactile piezoelectric sensors with neuromorphic transistors provides an alternative way to achieve perception and cognition functions for intelligent robots in human-machine interaction scenarios. To promote both long-term and short-term plasticity of the artificial synaptic transistor, a composite gate dielectric composed of ferroelectric terpolymer P(VDF-TrFE-CFE) and chitosan was intendedly developed, while amorphous metal oxide InZnO was adopted as the channel layer. The transition from short-term to long-term plasticity function was realized on the basis of the electric-double-layer effect and ferroelectric polarization. Benefiting from its low-voltage operation performance, this synaptic transistor was functionalized by connecting with a flexible piezoelectric poly(vinylidene fluoride) capacitor to exhibit tactile stimulus-excited synaptic behavior. Feedback control was further introduced into the tactile synaptic system to imitate two typical scenarios of sensation and response, including the action of a mechanical claw to pain sensation and spontaneous scratching to itch sensation. This work provides a perspective on achieving intelligent perception for soft robotics and healthcare application.

Multifunctional In-Memory Logics Based on a Dual-Gate Antiambipolar Transistor toward Non-von Neumann Computing Architecture

In-memory computing may make it possible to realize non-von Neumann computing because the logic circuits are unified in the memory units. We investigated two types of in-memory logic operations, namely, two-input logic circuits and multifunctional artificial synapses. These were realized in a dual-gate antiambipolar transistor (AAT) with a ReS2/WSe2 heterojunction, in which polystyrene with a zinc phthalocyanine core (ZnPc-PS4) was incorporated as a memory layer. Here, reconfigurability is a key concept for both types of device operations and was achieved by merging the Λ-shaped transfer curve of the AAT and the nonvolatile memory effect of ZnPc-PS4. First, we achieved electrically reconfigurable two-input logic circuits. Versatile logic circuits such as AND, OR, NAND, NOR, and XOR circuits were demonstrated by taking advantage of the Λ-shaped transfer curve of the dual-gate AAT. Importantly, the nonvolatile memory function provided the electrical switching of the individual circuits between AND/OR, NAND/NOR, and XOR/NAND circuits with constant input signals. Second, the memory effect was applied to multifunctional artificial synapses. The inhibitory/excitatory and long-term potentiation/depression synaptic operations were electrically reconfigured simply by controlling one parameter (readout voltage), making three distinct responses possible even with the same presynaptic signals. These findings provide hints that may lead to the realization of new in-memory computing architectures beyond the current von Neumann computers.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Spike rate dependent synaptic characteristics in lamellar, multilayered alpha-MoO3 based two-terminal devices - efficient way to control the synaptic amplification

Brain-inspired computing systems require a rich variety of neuromorphic devices using multi-functional materials operating at room temperature. Artificial synapses which can be operated using optical and electrical stimuli are in high demand. In this regard, layered materials have attracted a lot of attention due to their tunable energy gap and exotic properties. In the current study, we report the growth of layered MoO3 using the chemical vapor deposition (CVD) technique. MoO3 has an energy gap of 3.22 eV and grows with a large aspect ratio, as seen through optical and scanning electron microscopy. We used transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy for complete characterisation. The two-terminal devices using platinum (Pt/MoO3/Pt) exhibit superior memory with the high-resistance state (HRS) and low-resistance state (LRS) differing by a large resistance (∼MΩ). The devices also show excellent synaptic characteristics. Both optical and electrical pulses can be utilised to stimulate the synapse. Consistent learning (potentiation) and forgetting (depression) curves are measured. Transition from long term depression to long term potentiation can be achieved using the spike frequency dependent pulsing scheme. We have found that the amplification of postsynaptic current can be tuned using such frequency dependent spikes. This will help us to design neuromorphic devices with the required synaptic amplification.

Highly Reproducible Heterosynaptic Plasticity Enabled by MoS2/ZrO2-x Heterostructure Memtransistor

Electrically tunable resistive switching of a polycrystalline MoS₂-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS₂ grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS₂/ZrO_2-x heterostructure. Compared to the control device with the MoS₂ semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO_2-x layer below the MoS₂, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS₂/ZrO_2-x memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS₂/ZrO_2-x memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.

Time-Dependent Sensitivity Tunable pH Sensors Based on the Organic-Inorganic Hybrid Electric-Double-Layer Transistor

In this study, we propose tunable pH sensors based on the electric-double-layer transistor (EDLT) with time-dependent sensitivity characteristics. The EDLT is able to modulate the drain current by using the mobile ions inside the electrolytic gate dielectric. This property allows the implementation of a device with sensitivity characteristics that are simply adjusted according to the measurement time. An extended gate-type, ion-sensitive, field-effect transistor consisting of a chitosan/Ta2O5 hybrid dielectric EDLT transducer, and an SnO2 sensing membrane, were fabricated to evaluate the sensing behavior at different buffer pH levels. As a result, we were able to achieve tunable sensitivity by only adjusting the measurement time by using a single EDLT and without additional gate electrodes. In addition, to demonstrate the unique sensing behavior of the time-dependent tunable pH sensors based on organic−inorganic hybrid EDLT, comparative sensors consisting of a normal FET with a SiO2 gate dielectric were prepared. It was found that the proposed pH sensors exhibit repeatable and stable sensing operations with drain current deviations <1%. Therefore, pH sensors using a chitosan electrolytic EDLT are suitable for biosensor platforms, possessing tunable sensitivity and high-reliability characteristics.

Sputtered Electrolyte-Gated Transistor with Temperature-Modulated Synaptic Plasticity Behaviors

Temperature has always been considered as an essential factor for almost all kinds of semiconductor-based electronic components. In this work, temperature-dependent synaptic plasticity behaviors, which are mimicked by the indium-gallium-zinc oxide thin-film transistors gated with sputtered SiO2 electrolytes, have been studied. With the temperature increasing from 303 to 323 K, the electrolyte capacitance decreases from 0.42 to 0.11 μF cm-2. The mobility increases from 1.4 to 3.7 cm2 V-1 s-1, and the threshold voltage negatively shifts from -0.23 to -0.51 V. Synaptic behaviors under both a single pulse and multiple pulses are employed to study the temperature dependence. With the temperature increasing from 303 to 323 K, the post-synaptic current (PSC) at the resting state increases from 1.8 to 7.3 μA. Under a single gate pulse of 1 V and 1 s, the PSC signal altitude and the PSC retention time decrease from 2.0 to 0.7 μA and 5.1 × 102 to 2.5 ms, respectively. A physical model based on the electric field-induced ion drifting, ionic-electronic coupling, and gradient-coordinated ion diffusion is proposed to understand these temperature-dependent synaptic behaviors. Based on the experimental data on individual transistors, temperature-modulated pattern learning and memorizing behaviors are conceptually demonstrated. The in-depth investigation of the temperature dependence helps pave the way for further electrolyte-gated transistor-based neuromorphic applications.

Reconfigurable Artificial Synapses with Excitatory and Inhibitory Response Enabled by an Ambipolar Oxide Thin-Film Transistor

A gate-tunable synaptic device controlling dynamically reconfigurable excitatory and inhibitory synaptic responses, which can emulate the fundamental synaptic responses for developing diverse functionalities of the biological nervous system, was developed using ambipolar oxide semiconductor thin-film transistors (TFTs). Since the balanced ambipolarity is significant, a boron-incorporated SnO (SnO:B) oxide semiconductor channel was newly developed to improve the ambipolar charge transports by reducing the subgap defect density, which was reduced to less than 10¹⁷ cm^-3. The ambipolar SnO:B-TFT could be fabricated with a good reproductivity at the maximum process temperature of 250 °C and exhibited good TFT performances, such as a nearly zero switching voltage, the saturation mobility of ∼1.3 cm² V^-1 s^-1, s-value of ∼1.1 V decade^-1, and an on/off-current ratio of ∼8 × 10³ for the p-channel mode, while ∼0.14 cm² V^-1 s^-1, ∼2.2 V decade^-1and ∼1 × 10³ for n-channel modes, respectively. The ambipolar device imitated potentiation/depression behaviors in both excitatory and inhibitory synaptic responses by using the p- and n-channel transports by tuning a gate bias. The low-power consumptions of <20 and <2 nJ per pulse for the excitatory and inhibitory operations, respectively, were also achieved. The presented device operated under an ambient atmosphere and confirmed a good operation reliability over 5000 pulses and a long-term air environmental stability. The study presents the high potential of an ambipolar oxide-TFT-based synaptic device with a good manufacturability to develop emerging neuromorphic perception and computing hardware for next-generation artificial intelligence systems.

Sol-Gel Composites-Based Flexible and Transparent Amorphous Indium Gallium Zinc Oxide Thin-Film Synaptic Transistors for Wearable Intelligent Electronics

In this study, we propose the fabrication of sol-gel composite-based flexible and transparent synaptic transistors on polyimide (PI) substrates. Because a low thermal budget process is essential for the implementation of high-performance synaptic transistors on flexible PI substrates, microwave annealing (MWA) as a heat treatment process suitable for thermally vulnerable substrates was employed and compared to conventional thermal annealing (CTA). In addition, a solution-processed wide-bandgap amorphous In-Ga-Zn (2:1:1) oxide (a-IGZO) channel, an organic polymer chitosan electrolyte-based electric double layer (EDL), and a high-k Ta₂O₅ thin-film dielectric layer were applied to achieve high flexibility and transparency. The essential synaptic plasticity of the flexible and transparent synaptic transistors fabricated with the MWA process was demonstrated by single spike, paired-pulse facilitation, multi-spike facilitation excitatory post-synaptic current (EPSC), and three-cycle evaluation of potentiation and depression behaviors. Furthermore, we verified the mechanical robustness of the fabricated device through repeated bending tests and demonstrated that the electrical properties were stably maintained. As a result, the proposed sol-gel composite-based synaptic transistors are expected to serve as transparent and flexible intelligent electronic devices capable of stable neural operation.

Bi-mode electrolyte-gated synaptic transistor via additional ion doping and its application to artificial nociceptors

Multiple types of synaptic transistors that are capable of processing electrical signals similar to the biological neural system hold enormous potential for application in parallel computing, logic circuits and peripheral detection. However, most of these presented synaptic transistors are confined to a single mode of synaptic plasticity under an electrical stimulus, which tremendously limits efficient memory formation and the multifunctional integration of synaptic transistors. Here, we proposed a bi-mode electrolyte-gated synaptic transistor (BEST) with two dynamic processes, the formation of an electrical double layer (EDL) and electrochemical doping (ECD) by tuning the applied voltages, thereby allowing volatile and non-volatile behavior, which is associated with additional ion doping and nanoscale ionic transport. Benefiting from two controllable dynamic processes, we surprisingly found a third state in the transfer curves besides the "off" and "on" states. Moreover, utilizing this unique property, an artificial nociceptor with multilevel modulation of sensitivity was realized based on our bi-mode device. Finally, a haptic sensory system was constructed to exhibit robotic motion that revealed a unique threshold switching behavior, indicating the applicability to peripheral sensing circuits. Hence, the presented bi-mode synaptic transistor provides promising prospects in achieving multiple-mode integrated devices and simplifying neural circuits, which shows great potential in the development of artificial intelligence.

Lithography Processable Ta2O5 Barrier-Layered Chitosan Electric Double Layer Synaptic Transistors

We proposed a synaptic transistor gated using a Ta₂O₅ barrier-layered organic chitosan electric double layer (EDL) applicable to a micro-neural architecture system. In most of the previous studies, a single layer of chitosan electrolyte was unable to perform lithography processes due to poor mechanical/chemical resistance. To overcome this limitation, we laminated a high-k Ta₂O₅ thin film on chitosan electrolyte to ensure high mechanical/chemical stability to perform a lithographic process for micropattern formation. Artificial synaptic behaviors were realized by protonic mobile ion polarization in chitosan electrolytes. In addition, neuroplasticity modulation in the amorphous In–Ga–Zn-oxide (a-IGZO) channel was implemented by presynaptic stimulation. We also demonstrated synaptic weight changes through proton polarization, excitatory postsynaptic current modulations, and paired-pulse facilitation. According to the presynaptic stimulations, the magnitude of mobile proton polarization and the amount of weight change were quantified. Subsequently, the stable conductance modulation through repetitive potential and depression pulse was confirmed. Finally, we consider that proposed synaptic transistor is suitable for advanced micro-neural architecture because it overcomes the instability caused when using a single organic chitosan layer.

Bienenstock-Cooper-Munro Learning Rule Realized in Polysaccharide-Gated Synaptic Transistors with Tunable Threshold

With reference to the organization of the human brain nervous system, a hardware-based approach that builds massively parallel neuromorphic circuits is of great significance to neuromorphic computing. The Bienenstock-Cooper-Munro (BCM) learning rule, which describes that the synaptic weight modulation exhibits frequency-dependent and tunable frequency threshold characteristics, is more compatible with the working principle of neuromorphic computing systems than spike-timing-dependent plasticity. Therefore, it is interesting to simulate the BCM learning rule on solid-state synaptic devices. Here, we have prepared λ-carrageenan (λ-car) electrolyte-gated oxide synaptic transistors, which exhibit good transistor performances, including a low subthreshold swing of 125 mV/dec, an on/off ratio larger than 10⁶, and a mobility of 9.5 cm² V^-1 s^-1. By modulating the initial channel current and spike frequency, the simulation of the BCM rule was successfully realized. The competitive relationship between the drift of protons under an electric field and the spontaneous diffusion of protons can explain this mechanism. The proposed λ-car-gated synaptic transistor has a great significance to neuromorphic computing.

Flexible and Transparent Artificial Synapse Devices Based on Thin-Film Transistors with Nanometer Thickness

Background

Artificial synaptic behaviors are necessary to investigate and implement since they are considered to be a new computing mechanism for the analysis of complex brain information. However, flexible and transparent artificial synapse devices based on thin-film transistors (TFTs) still need further research.

Purpose

To study the application of flexible and transparent thin-film transistors with nanometer thickness on artificial synapses.

Materials and Methods

Here, we report the design and fabrication of flexible and transparent artificial synapse devices based on TFTs with polyethylene terephthalate (PET) as the flexible substrate, indium tin oxide (ITO) as the gate and a polyvinyl alcohol (PVA) grid insulating layer as the gate insulation layer at room temperature.

Results

The charge and discharge of the carriers in the flexible and transparent thin-film transistors with nanometer thickness can be used for artificial synaptic behavior.

Conclusion

In summary, flexible and transparent thin-film transistors with nanometer thickness can be used as pressure and temperature sensors. Besides, inherent charge transfer characteristics of indium gallium zinc oxide semiconductors have been employed to study the biological synapse-like behaviors, including synaptic plasticity, excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and long-term memory (LTM). More precisely, the spike rate plasticity (SRDP), one representative synaptic plasticity, has been demonstrated. Such TFTs are interesting for building future neuromorphic systems and provide a possibility to act as fundamental blocks for neuromorphic system applications.

Modulation of Binary Neuroplasticity in a Heterojunction-Based Ambipolar Transistor

To keep pace with the upcoming big-data era, the development of a device-level neuromorphic system with highly efficient computing paradigms is underway with numerous attempts. Synaptic transistors based on an all-solution processing method have received growing interest as building blocks for neuromorphic computing based on spikes. Here, we propose and experimentally demonstrated the dual operation mode in poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6-tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl}(PDPPBTT)/ZnO junction-based synaptic transistor from ambipolar charge-trapping mechanism to analog the spiking interfere with synaptic plasticity. The heterojunction formed by PDPPBTT and ZnO layers serves as the basis for hole-enhancement and electron-enhancement modes of the synaptic transistor. Distinctive synaptic responses of paired-pulse facilitation (PPF) and paired-pulse depression (PPD) were configured to achieve the training/recognition function for digit image patterns at the device-to-system level. The experimental results indicate the potential application of the ambipolar transistor in future neuromorphic intelligent systems.

Artificial synapses based on nanomaterials

Artificial synapses emulate biological synaptic signals in neuromorphic systems to attain brain-like computation and autonomous learning behaviors in non-von-Neumann systems. Several classes of materials have been applied to this field to achieve numerous functionalities of biological synapses. Nanomaterials (NMs), such as one-dimensional (1D) and two-dimensional (2D) NMs have shown great potential due to their nanometer feature size (1D) and molecular-level thickness (2D). In this paper, we review the development of artificial synapses, and discuss state-of-the-art artificial synapses based on NMs.

Chitosan-Based Polysaccharide-Gated Flexible Indium Tin Oxide Synaptic Transistor with Learning Abilities

Recently, environment-friendly electronic devices are attracting increasing interest. "Green" artificial synapses with learning abilities are also interesting for neuromorphic platforms. Here, solution-processed chitosan-based polysaccharide electrolyte-gated indium tin oxide (ITO) synaptic transistors are fabricated on polyethylene terephthalate substrate. Good transistor performances against mechanical stress are observed. Short-term synaptic plasticities are mimicked on the proposed ITO synaptic transistor. When applying presynaptic and postsynaptic spikes on gate electrode and drain electrode respectively, spike-timing-dependent plasticity function is mimicked on the synaptic transistor. Transitions from sensory memory to short-term memory (STM) and from STM to long-term memory are also mimicked, demonstrating a "multistore model" brain memory. Furthermore, the flexible ITO synaptic transistor can be dissolved in deionized water easily, indicating potential green neuromorphic platform applications.