Two-Terminal Lithium-Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks

Memristive Artificial Synapses Based on Brownmillerite for Endurable Weight Modulation

Exploring a computing paradigm that blends memory and computation functions is essential for artificial synapses. While memristors for artificial synapses are widely studied due to their energy-efficient structures, random filament conduction in general memristors makes them less preferred for endurability in long-term synaptic modulation. Herein, the topotactic phase transition (TPT) in brownmillerite-phased (110)-SrCoO2.5 (SCO2.5) is harnessed to enhance the reversibility of oxygen ion migration through 1-D oxygen vacancy channels. By employing a heteroepitaxial structured 2-terminal configuration of Au/SCO2.5/SrRuO3/SrTiO3, the brownmillerite SCO2.5-based synapse artificial synapses are exploited. Demonstration of the TPT behavior is corroborated by comparing oxygen migration energy by density-functional theory calculations and experimental results, and by monitoring the voltage pulse-induced peak shift in the Raman spectra of SCO2.5. With the voltage pulse-driven TPT behaviors, it is reliably characterized by linear, symmetric, and endurable long-term potentiation and depression performances. Notably, the durability of the TPT-based weight control mechanism is demonstrated by achieving consistent and noise-free weight updates over 32 000 iterations across 640 cycles. Furthermore, learning performances based on deep neural networks and convolutional neural networks on various image datasets yielded very high recognition accuracy. The work offers valuable insights into designing memristive synapses that enable reliable weight updates in neural networks.

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.

IGZO/WO3-x-Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture

Currently, artificial neural networks (ANNs) based on memristors are limited to recognizing static images of objects when simulating human visual system, preventing them from performing high-dimensional information perception, and achieving more complex biomimetic functions is subject to certain limitations. In this work, indium gallium zinc oxide (IGZO)/tungsten oxide (WO3-x)-heterostructured artificial optoelectronic synaptic devices mimicking image segmentation and motion capture exhibiting high-performance optoelectronic synaptic responses are proposed and demonstrated. Upon electrical and optical stimulations, the device shows a variety of fundamental and advanced electrical and optical synaptic plasticity. Most importantly, outstanding and repeatable linear synaptic weight changes are attained by the developed memristor. By taking advantage of the notable linear synaptic weight changes, ANNs have been constructed and successfully utilized to demonstrate two applications in the field of computer vision, including image segmentation and object tracking. The accuracy attained by the memristor-based ANNs is similar to that of the computer algorithms, while its power has been significantly reduced by 105 orders of magnitude. With successful emulations of the human brain reactions when observing objects, the demonstrated memristor and related ANNs can be effectively utilized in constructing artificial optoelectronic synaptic devices and show promising potential in emulating human visual perception.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

Mimicking Bidirectional Inhibitory Synapse Using a Porous-Confined Ionic Memristor with Electrolyte/Tris(4-aminophenyl)amine Neurotransmitter

Ionic memristors can emulate brain-like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory-excitatory and excitatory-inhibitory synapses, reports on memristors with the inhibitory-inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de-trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature-inferencing/-training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices.

Artificial Synapse Based on a δ-FAPbI3/Atomic-Layer-Deposited SnO2 Bilayer Memristor

Halide perovskite-based resistive switching memory (memristor) has potential in an artificial synapse. However, an abrupt switch behavior observed for a formamidinium lead triiodide (FAPbI3)-based memristor is undesirable for an artificial synapse. Here, we report on the δ-FAPbI3/atomic-layer-deposited (ALD)-SnO2 bilayer memristor for gradual analogue resistive switching. In comparison to a single-layer δ-FAPbI3 memristor, the heterojunction δ-FAPbI3/ALD-SnO2 bilayer effectively reduces the current level in the high-resistance state. The analog resistive switching characteristics of δ-FAPbI3/ALD-SnO2 demonstrate exceptional linearity and potentiation/depression performance, resembling an artificial synapse for neuromorphic computing. The nonlinearity of long-term potentiation and long-term depression is notably decreased from 12.26 to 0.60 and from -8.79 to -3.47, respectively. Moreover, the δ-FAPbI3/ALD-SnO2 bilayer achieves a recognition rate of ≤94.04% based on the modified National Institute of Standards and Technology database (MNIST), establishing its potential in an efficient artificial synapse.

Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.

Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception

The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W-1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and "learning-experience" behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.

Nanoelectronics Using Metal-Insulator Transition

Metal-insulator transition (MIT) coupled with an ultrafast, significant, and reversible resistive change in Mott insulators has attracted tremendous interest for investigation into next-generation electronic and optoelectronic devices, as well as a fundamental understanding of condensed matter systems. Although the mechanism of MIT in Mott insulators is still controversial, great efforts have been made to understand and modulate MIT behavior for various electronic and optoelectronic applications. In this review, recent progress in the field of nanoelectronics utilizing MIT is highlighted. A brief introduction to the physics of MIT and its underlying mechanisms is begun. After discussing the MIT behaviors of various Mott insulators, recent advances in the design and fabrication of nanoelectronics devices based on MIT, including memories, gas sensors, photodetectors, logic circuits, and artificial neural networks are described. Finally, an outlook on the development and future applications of nanoelectronics utilizing MIT is provided. This review can serve as an overview and a comprehensive understanding of the design of MIT-based nanoelectronics for future electronic and optoelectronic devices.

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.

Halide Perovskites-Based Diffusive Memristors for Artificial Mechano-Nociceptive System

Numerous efforts for emulating organ systems comprised of multiple functional units have driven substantial advancements in bio-realistic electronics and systems. The resistance change behavior observed in diffusive memristors shares similarities with the potential change in biological neurons. Here, the diffusive threshold switching phenomenon in Ag-incorporated organometallic halide perovskites is utilized to demonstrate the functions of afferent neurons. Halide perovskites-based diffusive memristors show a low threshold voltage of ≈0.2 V with little variation, attributed to the facile migration of Ag ions uniformly dispersed within the halide matrix. Based on the reversible and reliable volatile threshold switching, the memristors successfully demonstrate fundamental nociceptive functions including threshold firing, relaxation, and sensitization. Furthermore, to replicate the biological mechano-nociceptive phenomenon at a system level, an artificial mechano-nociceptive system is built by integrating a diffusive memristor with a force-sensing resistor. The presented system is capable of detecting and discerning the detrimental impact caused by a heavy steel ball, effectively exhibiting the corresponding sensitization response. By further extending the single nociceptive system into a 5 × 5 array, successful stereoscopic nociception of uneven impulses is achieved in the artificial skin system through array-scale sensitization. These results represent significant progress in the field of bio-inspired electronics and systems.