Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices

Two-Dimensional Covalent Organic Frameworks in Organic Electronics

Two-dimensional covalent organic frameworks (2DCOFs) are a unique class of crystalline porous materials interconnected by covalent bonds, which have attracted significant attention in recent years due to their chemical and structural diversity, as well as their applications in adsorption, separation, catalysis, and drug delivery. However, research on the electrical properties of 2DCOFs remains limited, despite their potential in organic electronics. Early studies recognized the poor electrical conductivity of 2DCOFs as a significant obstacle to their application in this field. To overcome this challenge, various strategies have been proposed to enhance conductivity. This review first introduces the concept of computational screening for 2DCOFs and explores approaches to improve their intrinsic conductivity, with a focus on four key aspects: in-plane and out-of-plane charge transport, topology, bandgap, and morphology. It then examines the application of pristine 2DCOFs in organic electronics, including applications in field-effect transistors, memristors, photodetectors, and chemiresistive gas sensors. We support these strategies with detailed statistical data, providing a comprehensive guide for the design and development of novel 2DCOFs for organic electronics. Finally, we outline future research directions, emphasizing the challenges that remain to be addressed in this emerging area.

Revealing the Effect of Substantial A Site Cation Multiplicity in Lead-Free Double Perovskites for Memristor-Based Synaptic Devices

In the realm of memory storage and brain-inspired neural computing, memristors stand out as some of the most rapidly advancing electronic components. In this aspect, Halide perovskite-based two-terminal memristors have gained widespread usage in energy-efficient artificial synapses and resistive random-access memory systems. Their appeal lies in their lower operating voltage, high ON/OFF ratio, cost-effective production, and reliable photoelectric regulation performance. In this study, we have successfully harnessed a one-step chemical synthesis method to fabricate a series of lead-free double perovskite ((Cs1-xRbx)2AgBiBr6, x = 0,0.1,0.15). While the pure Cs2AgBiBr6 device lacked discernible switching characteristics, introducing Rb dopants resulted in robust resistive switching attributed to defect states from the Cs site Rb substitution. Dopant concentration is tailored to enhance device performance─durability, reproducibility, mechanical flexibility, and ON/OFF ratio. Additionally, we confirmed the valence change mechanism through temperature-dependent conductivity of the high resistive states and SET/RESET voltage analysis. Remarkably, resistance of flexible memory devices remains unaffected by bending curvature. Furthermore, our two-terminal synaptic device effectively regulates voltage pulses, mimicking biological synapses. Pulse measurements emulate fundamental synaptic functions: excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation, and long-term depression under normal conditions. When applied to handwritten digit recognition simulation, these synaptic memristors achieved a high accuracy rate of 96.6%. These findings hold promise for next-gen electronics with lead-free Rb-doped double perovskite-based artificial synapses.

Coordination bonds as a tool for tuning photoconductance in nanostructured hybrid materials made of molecular antennas and metal nanoparticles

The synthesis of robust, versatile materials in which electrical conduction is enhanced by light irradiation is of prime importance for fields as varied as photodetectors, photodiodes, solar cells and light sensors. Hybrid materials offer the advantage of combining the robustness of an inorganic building block with the adaptability of a molecular subunit. Herein, we demonstrate the importance of properly investigating the nature of the chemical interactions between the constituent elements in order to optimize photoconductance within hybrid materials. To this end, platinum nanoparticle self-assemblies are synthesized in solution, including a series of zinc-porphyrins differentially functionalized with pyridine moieties in the meso position. The presence of coordinating groups on the molecular entities drastically reinforced both the structural cohesion of the system and its photoconductive properties.

Photodual-Responsive Anthracene-Based 2D Covalent Organic Framework for Optoelectronic Synaptic Devices

To facilitate the development of efficient neuromorphic perception and computation, it is crucial to explore optoelectronic synaptic devices that integrate perceptual and computational capabilities. Various materials such as oxide semiconductors, conjugated organic polymers, transition metal sulfides, perovskite materials, and metal nanoparticles, along with their composites, are utilized in constructing these devices. However, optoelectronic synaptic devices based on 2D covalent organic frameworks (COFs) is rarely reported. In this study, an anthracene-based 2D COF (COF-DaTp) film is prepared using a room-temperature interface-confined strategy and utilized it as the active layer in an optoelectronic synaptic device with an Al/COF-DaTp/ITO configuration. The device demonstrated dual optoelectronic modulation, exhibiting significant optoelectronic resistive switching in response to light pulses, achieving 32 photoconductive states. Moreover, it exhibited history-dependent memristive behavior in voltage scans and electrical pulses, with a comparable diversity of 32 conductive states. The photodual-responsive properties of the COF-DaTp-based synaptic device enable it to simultaneously perform optical sensing and basic image denoising and recognition tasks, significantly enhancing recognition accuracy and reducing the number of training epochs compared to datasets without noise mitigation. This work opens the door for the application of 2D COF-based optoelectronic synaptic devices in visual computational processing.

Optoelectric coordinated modulation of resistive switching behavior in perovskite based synaptic device

Triple cation halide perovskite (TCP) stands out as a superior photoelectric material, with a broader absorption range, higher absorption efficiency, and improved environmental stability. Due to its excellent synaptic plasticity, TCP facilitates advanced neural morphological operations like light-assisted learning. Here, a modifying layer of polythiophene (P3HT) was incorporated onto the TCP thin film to enhance the resistive switching (RS) characteristics of the synaptic device, which exhibits excellent stability (103 endurance cycles and > 103 s retention time) and low energy consumption (~ 6.3 pJ for electrical stimulus and ~ 6 pJ for optical stimulus). Additionally, the synaptic properties of the perovskite / P3HT heterojunction synaptic device were explored under optoelectric coordinated modulation, encompassing Long-Term Potentiation (LTP), Long-Term Depression (LTD), frequency-dependent plasticity (SRDP) and voltage-dependent plasticity (SVDP). By leveraging the linear characteristics of synaptic plasticity, arithmetic operations, Pavlovian conditioned reflex and vision recognition are achieved. The recognition accuracies of 89.8% / 88.1% (electric synapse) are enhanced to 92.4% / 92.2% after the introduction of optoelectronic cooperative stimulation on the 8 × 8 and 28 × 28 modified national institute of standards and technology (MNIST) handwritten digit datasets. This study holds significant implications for guiding the optoelectronic co-regulation of perovskite synaptic devices in the field of synaptic electronics.

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.

Recent advances in artificial neuromorphic applications based on perovskite composites

High-performance perovskite materials with excellent physical, electronic, and optical properties play a significant role in artificial neuromorphic devices. However, the development of perovskites in microelectronics is inevitably hindered by their intrinsic non-ideal properties, such as high defect density, environmental sensitivity, and toxicity. By leveraging materials engineering, integrating various materials with perovskites to leverage their mutual strengths presents great potential to enhance ion migration, energy level alignment, photoresponsivity, and surface passivation, thereby advancing optoelectronic and neuromorphic device development. This review initially provides an overview of perovskite materials across different dimensions, highlighting their physical properties and detailing their applications and metrics in two- and three-terminal devices. Subsequently, we comprehensively summarize the application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and crystalline porous materials (CPMs), to develop advanced devices such as memristors, transistors, photodetectors, sensors, light-emitting diodes (LEDs), and artificial neuromorphic systems. Lastly, we outline the challenges and future research directions in synthesizing perovskite composites for neuromorphic devices. Through the review and analysis, we aim to broaden the utilization of perovskites and their composites in neuromorphic research, offering new insights and approaches for grasping the intricate physical working mechanisms and functionalities of perovskites.

An evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices

In the field of neuroscience, significant progress has been made regarding how the brain processes information. Unlike computer processors, the brain comprises neurons and synapses instead of memory blocks and transistors. Despite advancements in artificial neural networks, a complete understanding concerning brain functions remains elusive. For example, to achieve more accurate neuron replication, we must better understand signal transmission during synaptic processes, neural network tunability, and the creation of nanodevices featuring neurons and synapses. This study discusses the latest algorithms utilized in neuromorphic systems, the production of synaptic devices, differences between single and multisensory gadgets, recent advances in multisensory devices, and the promising research opportunities available in this field. We also explored the ability of an artificial synaptic device to mimic biological neural systems across diverse applications. Despite existing challenges, neuroscience-based computing technology holds promise for attracting scientists seeking to enhance solutions and augment the capabilities of neuromorphic devices, thereby fostering future breakthroughs in algorithms and the widespread application of cutting-edge technologies.

Heterointerface engineering of layered double hydroxide/MAPbBr3 heterostructures enabling tunable synapse behaviors in a two-terminal optoelectronic device

Solution-processable semiconductor heterostructures enable scalable fabrication of high performance electronic and optoelectronic devices with tunable functions via heterointerface control. In particular, artificial optical synapses require interface manipulation for nonlinear signal processing. However, the limited combinations of materials for heterostructure construction have restricted the tunability of synaptic behaviors with simple device configurations. Herein, MAPbBr3 nanocrystals were hybridized with MgAl layered double hydroxide (LDH) nanoplates through a room temperature self-assembly process. The formation of such heterostructures, which exhibited an epitaxial relationship, enabled effective hole transfer from MAPbBr3 to LDH, and greatly reduced the defect states in MAPbBr3. Importantly, the ion-conductive nature of LDH and its ability to form a charged surface layer even under low humidity conditions allowed it to attract and trap holes from MAPbBr3. This imparted tunable synaptic behaviors and short-term plasticity (STP) to long-term plasticity (LTP) transition to a two-terminal device based on the LDH-MAPbBr3 heterostructures. The further neuromorphic computing simulation under varying humidity conditions showcased their potential in learning and recognition tasks under ambient conditions. Our work presents a new type of epitaxial heterostructure comprising metal halide perovskites and layered ion-conductive materials, and provides a new way of realizing charge-trapping induced synaptic behaviors.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Element Regulation and Dimensional Engineering Co-Optimization of Perovskite Memristors for Synaptic Plasticity Applications

Capitalizing on rapid carrier migration characteristics and outstanding photoelectric conversion performance, halide perovskite memristors demonstrate an exceptional resistive switching performance. However, they have consistently faced constraints due to material stability issues. This study systematically employs elemental modulation and dimension engineering to effectively control perovskite memristors with different dimensions and A-site elements. Compared to pure 3D and 2D perovskites, the quasi-2D perovskite memristor, specifically BA0.15MA0.85PbI3, is identified as the optimal choice through observations of resistive switching (HRS current < 10-5 A, ON/OFF ratio > 103, endurance cycles > 1000, and retention time > 104 s) and synaptic plasticity characteristics. Subsequently, a comprehensive investigation into various synaptic plasticity aspects, including paired-pulse facilitation (PPF), spike-variability-dependent plasticity (SVDP), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP), is conducted. Practical applications, such as memory-forgetting-memory and recognition of the Modified National Institute of Standards and Technology (MNIST) database handwritten data set (accuracy rate reaching 94.8%), are explored and successfully realized. This article provides good theoretical guidance for synaptic-like simulation in perovskite memristors.

Orientational effects in the polarized absorption spectra of molecular aggregates

We present a detailed theoretical analysis of polarized absorption spectra and linear dichroism of cyanine dye aggregates whose unit cells contain two molecules. The studied threadlike ordered system with a molecular exciton delocalized along its axis can be treated as two chains of conventional molecular aggregates, rotated relative to each other at a certain angle around the aggregate axis. Our approach is based on the general formulas for the effective cross section of light absorption by a molecular aggregate and key points of the molecular exciton theory. We have developed a self-consistent theory for describing the orientational effects in the absorption and dichroic spectra of such supramolecular structures with nonplanar unit cell. It is shown that the spectral behavior of such systems exhibits considerable distinctions from that of conventional cyanine dye aggregates. They consist in the strong dependence of the relative intensities of the J- and H-type spectral bands of the aggregate with a nonplanar unit cell on the angles determining the mutual orientations of the transition dipole moments of constituting molecules and the aggregate axis as well as on the polarization direction of incident light. The derived formulas are reduced to the well-known analytical expressions in the particular case of aggregates with one molecule in the unit cell. The calculations performed within the framework of our excitonic theory combined with available vibronic theory allow us to quite reasonably explain the experimental data for the pseudoisocyanine bromide dye aggregate.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.