Spike-rate-dependent Plasticity Research Articles

Photo‐synaptic Memristor Devices from Solution‐processed Ga2O3 Thin Films

AbstractHardware integration with biological synaptic function is the key to realizing brain‐like computing. Resistive Random Access Memory (RRAM), with a similar structure to biological synapses, are important candidate for the simulation of biological synaptic function. In this work, Ga2O3 film as a functional layer of RRAM is prepared by the solution method, and an RRAM‐based photo‐synaptic device with an Ag/Ga2O3/Si structure is constructed subsequently. The device exhibits excellent bipolar resistive switching characteristics, with the merits of a large storage window and long retention time. Furthermore, the devices generated excitatory postsynaptic currents (EPSC) and paired‐pulse facilitation (PPF) behaviors under light pulse stimulation, enabling the simulation of synaptic plasticity. The transformation of synaptic behavior from short‐term memory (STM) to long‐term memory (LTM) is achieved by observing the spike‐duration dependent plasticity (SDDP), spike‐intensity dependent plasticity (SIDP), spike‐number dependent plasticity (SNDP) and spike‐rate dependent plasticity (SRDP) characteristics of photonic synapses under different conditions. The device also simulates the process of successive “learning‐forgotten‐remembering”, revealing that RRAM‐based photonic synapses have great potential in the fields of artificial visual perception and memory storage.

Bioinspired Flexible and Low-Voltage Organic Synaptic Transistors for UV Light-Driven Vision Systems.

Neuromorphic vision systems, particularly those stimulated by ultraviolet (UV) light, hold great potential applications in portable electronics, wearable technology, biological analysis, military surveillance, etc. Organic artificial synaptic devices hold immense potential in this field due to their ease of processing, flexibility, and biocompatibility. In this work, we have fabricated a flexible organic field-effect transistor (OFET) that utilizes chitosan-silver nanoparticles (AgNPs) composite material as the active dielectric material. During UV light illumination, both silver nanoparticles and the pentacene layer generate a large number of charge carriers. The photogenerated carriers lead to a more significant hole accumulation at the pentacene interface, resulting in a current rise. In the absence of light, the trapped electron in the silver nanoparticles persists for a longer duration, preventing the instant recombination with holes. This extended retention of electrons leads to the observed synaptic performance of the transistor. The use of aluminum oxide (Al2O3) as one of the dielectric layers enables the device to operate effectively at low voltage (<1 V). The device mimics various crucial synaptic properties of the brain, including short-term potentiation and long-term potentiation (STP and LTP), paired-pulse facilitation (PPF), spike-duration dependent plasticity (SDDP), spike-number dependent plasticity (SNDP), and spike-rate dependent plasticity (SRDP), etc. This work introduces an approach to develop flexible organic synaptic transistors that operate efficiently at low voltages, paving the way toward high-performance, UV light-driven neuromorphic vision systems.

Neuromorphic learning and recognition in WO3−x thin film-based forming-free flexible electronic synapses

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing transition from STP to LTP. Furthermore, other variables are investigated, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity. Utilizing data from flat and differently bent synapses, neural network simulations for pattern recognition tasks using the Modified National Institute of Standards and Technology dataset reveal a high recognition accuracy of ∼95% with a fast learning speed that requires only 15 epochs to reach saturation.

Volatile and Nonvolatile Programmable Iontronic Memristor with Lithium Imbued TiOx for Neuromorphic Computing Applications.

We demonstrate a lithium (Li) imbued TiOx iontronic device that exhibits synapse-like short-term plasticity behavior without requiring a forming process beforehand or a compliance current during switching. A solid-state electrolyte lithium phosphorus oxynitride (LiPON) behaves as the ion source, and the embedding and releasing of Li ions inside the cathodic like TiOx renders volatile conductance responses from the device and offers a natural platform for hardware simulating neuron functionalities. Besides, these devices possess high uniformity and great endurance as no conductive filaments are present. Different short-term pulse-based phenomena, including paired pulse facilitation, post-tetanic potentiation, and spike rate-dependent plasticity, were observed with self-relaxation characteristics. Based on the voltage excitation period, the time scale of the volatile memory can be tuned. Temperature measurement reveals the ion displacement-induced conductance channels become frozen below 220 K. In addition, the volatile analog devices can be configured into nonvolatile memory units with multibit storage capabilities after an electroforming process. Therefore, on the same platform, we can configure volatile units as nonlinear dynamic reservoirs for performing neuromorphic training and the nonvolatile units as the weight storage layer. We proceed to use voice recognition as an example with the tunable time constant relationship and obtain 94.4% accuracy with a minimal training data set. Thus, this iontronic platform can effectively process and update temporal information for reservoir and neuromorphic computing paradigms.

Effect of neural firing pattern on NbOx/Al2O3 memristor-based reservoir computing system

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (&amp;gt;103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between “0” and “1.”

Spike-enhanced synapse functions of SnOx-based resistive memory

This paper explores the application of resistive random-access memory (RRAM) devices in emulating various synapse functions. The electrical properties of an indium tin oxide (ITO)/SnOx/TaN device are examined, including the activation process, switching process, endurance (>102), and retention characteristics (104 s). A conduction mechanism for the resistive-switching process based on the migration of oxygen ions in the insulating SnOx film is proposed. Furthermore, the multilevel cell characteristics of the device, demonstrating the control of resistance states by variations in reset voltage and compliance current, were found to be suitable, increasing its data storage capacity. Synapse functions, including potentiation and depression, are tested, and their repeatability is evaluated. A neural network-based Modified National Institute Standards and Technology pattern recognition system is applied to the ITO/SnOx/TaN device, showcasing its capabilities in pattern recognition tasks. This study further explores various synapse functions, such as paired-pulse facilitation, paired-pulse depression, excitatory postsynaptic current, and activity-dependent synaptic plasticity. Hebbian learning rules, including spike rate-dependent plasticity, are demonstrated, along with spike time-dependent plasticity under various spike types, making it suitable for high-functionality neuromorphic applications. Finally, we assembled 3 × 3 synapse arrays and utilized them to form recognizable patterns, demonstrating the potential of the device in implementing neural network functions.

Improvement of synaptic property of GeSe based CBRAM by engineering the deposition condition of switching matrix

A study of effect of deposition condition of germanium selenide (GeSe) switching layer to Conductive bridging random access memory (CBRAM) device property was conducted to enhance memory property and synaptic property. The devices deposited under the varied deposition conditions were analyzed by current distribution of each state (Low-resistance state [LRS], High-resistance state [HRS]), conduction mechanism analysis, chemical analysis, and synaptic analysis according to pulse conditions. When we deposited switching layer at lowest power (40 W), more Ag ions move to the switching matrix and make lower operation voltage. But there were some disadvantages, which were worst distribution and higher HRS current, less memory window, nonlinear and rapid increase of potentiation process. Conversely, although the device deposited at high voltage increased the operating voltage, but there are improvements, such as the highest memory window and improved linearity of potentiation. The best sample was conducted the synaptic property measurement, and the device successfully mimicked the biological synaptic property with the good linearity of potentiation and depression, spike-rate dependent plasticity (SRDP) and spike-timing dependent plasticity (STDP) properties.

Broadband Artificial Tetrachromatic Synaptic Devices Composed of 2D/3D Integrated WSe2‐GaN‐based Dual‐Channel Floating Gate Transistors

AbstractThe development of artificial tetrachromatic vision holds great potential to enhance human color perception and discrimination, thereby enabling more effective navigation in diverse environments. Herein, an artificial tetrachromatic synaptic device is presented built upon 2D‐3D vertically stacked semiconductors composed of tungsten diselenide (WSe2)‐gallium nitride (GaN) configuration, forming a dual‐channel floating gate transistor (FGT). Under the concerted influence of electrical and optical stimulation, the device successfully mimics fundamental tetrachromatic synaptic behaviors, including short‐term potentiation (STP), weak long‐term potentiation (wLTP), long‐term potentiation (LTP), paired‐pulse facilitation (PPF), spike number‐dependent plasticity (SNDP), and spike rate‐dependent plasticity (SRDP). Notably, the plasticity of the device can be further modulated under ultraviolet (UV) stimulation, providing insights into the modulation of synaptic plasticity through the photogenerated carrier dynamics in the GaN channel. These results imply that WSe2‐GaN‐based FGT architecture with dual‐channel characteristics seamlessly integrates optical sensing and synaptic simulation functionalities, representing a promising avenue for the development of next‐generation artificial visual perception systems (AVPS), with a particular advantage for the pursuit of high‐performance artificial tetrachromatic neuromorphic computing applications of the future.

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.

Implementation of Artificial Synapse Using IGZO-Based Resistive Switching Device.

In this study, we present the resistive switching characteristics and the emulation of a biological synapse using the ITO/IGZO/TaN device. The device demonstrates efficient energy consumption, featuring low current resistive switching with minimal set and reset voltages. Furthermore, we establish that the device exhibits typical bipolar resistive switching with the coexistence of non-volatile and volatile memory properties by controlling the compliance during resistive switching phenomena. Utilizing the IGZO-based RRAM device with an appropriate pulse scheme, we emulate a biological synapse based on its electrical properties. Our assessments include potentiation and depression, a pattern recognition system based on neural networks, paired-pulse facilitation, excitatory post-synaptic current, and spike-amplitude dependent plasticity. These assessments confirm the device's effective emulation of a biological synapse, incorporating both volatile and non-volatile functions. Furthermore, through spike-rate dependent plasticity and spike-timing dependent plasticity of the Hebbian learning rules, high-order synapse imitation was done.

Humidity-Induced Protein-Based Artificial Synaptic Devices for Neuroprosthetic Applications.

Neuroprosthetics and brain-machine interfaces are immensely beneficial for people with neurological disabilities, and the future generation of neural repair systems will utilize neuromorphic devices for the advantages of energy efficiency and real-time performance abilities. Conventional synaptic devices are not compatible to work in such conditions. The cerebrospinal fluid (CSF) in the central part of the nervous system is composed of 99% water. Therefore, artificial synaptic devices, which are the fundamental component of neuromorphic devices, should resemble biological nerves while being biocompatible, and functional in high-humidity environments with higher functional stability for real-time applications in the human body. In this work, artificial synaptic devices are fabricated based on gelatin-PEDOT: PSScomposite as an active material to work more effectively in a highly humid environment (≈90% relative humidity). These devices successfully mimic various synaptic properties by the continuous variation of conductance, like, excitatory/inhibitory post-synaptic current(EPSC/IPSC), paired-pulse facilitation/depression(PPF/PPD), spike-voltage dependent plasticity (SVDP), spike-duration dependent plasticity (SDDP), and spike-rate dependent plasticity (SRDP) in environments at a relative humidity levels of ≈90%.

Synaptic Properties and Short‐Term Memory Dynamics of TiO2/WOx Heterojunction Memristor for Reservoir Computing

AbstractA hard breakdown phenomenon occurs in the TiN/WOX/Pt device owing to the metallic nature of the WOX layer deposited by pulsed direct current (DC) sputtering. In particular, analog resistive switching (RS) is achieved as the defect states of the naturally occurring TiON layer (oxygen vacancies region) between TiN and TiO2 fluctuate based on the polarity of the bias. Interestingly, the TiN/TiO2/WOX/Pt device displays gradual, bipolar, SET and RESET operations during DC voltage sweep cycling without requiring an electroforming process. Excellent linearity in potentiation and depression is demonstrated via identical pulse trains based on the analog RS behavior. Additionally, the neuromorphic system simulation achieved a pattern‐recognition accuracy of over 95% when conductance is employed as the weight in the neural network. Furthermore, essential synaptic functions, such as spike‐rate‐dependent plasticity (SRDP), spike‐number‐dependent plasticity (SNDP), the transition from short‐term plasticity to long‐term plasticity, “learning‐experience” behaviors, and paired‐pulse facilitation (PPF), are demonstrated to emulate biological synapses for neuromorphic computing applications. Lastly, a reservoir computing system (RC) is implemented using the short‐term memory effect of the TiN/TiO2/WOX/Pt device. Specifically, it is deployed to differentiate all 16 (4‐bit) states using various pulse trains, and a simple algorithm is suggested to implement a low‐power consumption system.

Combined optical and electrical control of a low-power consuming (∼fJ) two-terminal organic artificial synapse for associative learning and neuromorphic applications.

Optoelectronic synaptic devices outperform electrical synapses in speed, energy efficiency, and integration density. Recent progress in visual sensing and optogenetics has led to the integration of light-sensitive materials in these devices, promising unmatched speed, connectivity, and bandwidth. Here, we present a copper phthalocyanine (CuPc) based optoelectronic synaptic device boasting femto Joule power consumption stable at room temperature. The optoelectronic synapse can be operated with energy consumption as low as 430.4 fJ which is very attractive from the point of view of low-power neuromorphic devices. By modulating light pulses, the neuromorphic behavior can be emulated including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), transitioning from short-term plasticity (STP) to long-term plasticity (LTP), spike-rate dependent plasticity (SRDP) and spike-number dependent plasticity (SNDP), etc. Optical potentiation and electrical depression are observed with combined optical and electrical stimulation, proving the multi-functionality of the synapse. Furthermore, the device demonstrates classical associative learning behaviors like Pavlovian conditioning using optical and electrical stimuli. We have established the pain conditioning processes such as hyperalgesic response and pain extinction effects with varying optical pulse amplitudes. These results render the CuPc-based devices as multifunctional and highly versatile artificial synaptic devices for future computing applications, offering unprecedented efficiency and functionality in neuromorphic systems.

Optoelectronic synapses based on a triple cation perovskite and Al/MoO3 interface for neuromorphic information processing.

Optoelectronic synaptic transistors are attractive for applications in next-generation brain-like computation systems, especially for their visible-light operation and in-sensor computing capabilities. However, from a material perspective, it is difficult to build a device that meets expectations in terms of both its functions and power consumption, prompting the call for greater innovation in materials and device construction. In this study, we innovatively combined a novel perovskite carrier supply layer with an Al/MoO3 interface carrier regulatory layer to fabricate optoelectronic synaptic devices, namely Al/MoO3/CsFAMA/ITO transistors. The device could mimic a variety of biological synaptic functions and required ultralow-power consumption during operation with an ultrafast speed of >0.1 μs under an optical stimulus of about 3 fJ, which is equivalent to biological synapses. Moreover, Pavlovian conditioning and visual perception tasks could be implemented using the spike-number-dependent plasticity (SNDP) and spike-rate-dependent plasticity (SRDP). This study suggests that the proposed CsFAMA synapse with an Al/MoO3 interface has the potential for ultralow-power neuromorphic information processing.

Lead-free, highly-stable methyl ammonium bismuth halide perovskite memristors for mimicking biological synapses

In this work, lead-free and non-toxic (CH3NH3)3Bi2I9 (abbreviated as MABI) films are synthesized using a two-step sol-gel method, and FTO/MABI/Ag memristors are fabricated based on MABI films. The memristive behaviors in the devices were studied, showing controllable resistance change under voltage sweeps as well as pulses. The memristors are capable of mimicking the plasticity of biological synapses by setting pulse parameters, such as paired pulse facilitation (PPF), long-term plasticity, spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), and spike-number-dependent plasticity (SNDP). Moreover, the cycle of “learning-forgetting-relearning” typically observed in human learning can be also realized by the memristor. Based on study of current conduction behavior and the influence of top electrodes on I–V hysteresis in the memristors, it is concluded that the formation and rupture of conductive filaments formed by iodine vacancies in the MABI films are responsible for memristive characteristics. The MABI film and FTO/MABI/Ag memristor demonstrated good stability in a three-month test conducted in ambient air. The lead-free, non-toxic, and stable MABI-based memristors can be used promisingly for neuromorphic computing and machine learning in the future.

Artificial synapses based on 2D-layered palladium diselenide heterostructure dynamic memristor for neuromorphic applications

The transformation of partially amorphous two-dimensional (2D) material layers represents a promising avenue for enhancing the reliability of heterostructure memristor devices and achieving high-density memory with synaptic characteristics. In this study, we investigate the advantages of aluminum oxide-based redox reactive layers integrated with 2D pentagonal palladium diselenide (PdSe2) layers, resulting in the realization of a reliable multilevel conductance memory controlled by DC and pulse voltages. Through careful analysis, we observed that the formation and rupture of conductive filaments related to oxygen vacancies were significantly controlled at the Al2O3/PdSe2 interface, where a mixed oxide of Al-PdSeOx was formed. This exciting phenomenon was further corroborated by comprehensive chemical analysis of the synapse structure as well as various synaptic behaviors, predominantly modulated by the facile movement of oxygen ions under the influence of external electric fields. Remarkably, our experimental results demonstrated improved endurance with multiple memory states achieved by judiciously altering the RESET voltage and SET current compliance, yielding an impressive memory window exceeding 10. Moreover, we successfully achieve a controlled transition from short-term to long-term memory through the application of identical and nonidentical consecutive pulse voltages. Inspired by biological synapses, our PdSe2-based artificial synapse exhibits biocompatible short-term plasticity, such as spike rate-dependent plasticity, paired-pulse facilitation, and experience-dependent plasticity, effectively mimicking the functionality of its biological counterparts. Notably, this biocompatible synapse also demonstrates great potential for visual memory processing, and multilevel 4-bit reservoir computing by demonstrating 5 × 4 digit image recognition thereby offering a distinct advantage for artificial neuromorphic applications.

Analog Memory and Synaptic Plasticity in an InGaZnO-Based Memristor by Modifying Intrinsic Oxygen Vacancies.

This study focuses on InGaZnO-based synaptic devices fabricated using reactive radiofrequency sputtering deposition with highly uniform and reliable multilevel memory states. Electron trapping and trap generation behaviors were examined based on current compliance adjustments and constant voltage stressing on the ITO/InGaZnO/ITO memristor. Using O2 + N2 plasma treatment resulted in stable and consistent cycle-to-cycle memory switching with an average memory window of ~95.3. Multilevel resistance states ranging from 0.68 to 140.7 kΩ were achieved by controlling the VRESET within the range of -1.4 to -1.8 V. The modulation of synaptic weight for short-term plasticity was simulated by applying voltage pulses with increasing amplitudes after the formation of a weak conductive filament. To emulate several synaptic behaviors in InGaZnO-based memristors, variations in the pulse interval were used for paired-pulse facilitation and pulse frequency-dependent spike rate-dependent plasticity. Long-term potentiation and depression are also observed after strong conductive filaments form at higher current compliance in the switching layer. Hence, the ITO/InGaZnO/ITO memristor holds promise for high-performance synaptic device applications.

Artificial Synapses Based on an Optical/Electrical Biomemristor

As artificial synapse devices, memristors have attracted widespread attention in the field of neuromorphic computing. In this paper, Al/polymethyl methacrylate (PMMA)/egg albumen (EA)–graphene quantum dots (GQDs)/PMMA/indium tin oxide (ITO) electrically/optically tunable biomemristors were fabricated using the egg protein as a dielectric layer. The electrons in the GQDs were injected from the quantum dots into the dielectric layer or into the adjacent quantum dots under the excitation of light, and the EA–GQDs dielectric layer formed a pathway composed of GQDs for electronic transmission. The device successfully performed nine brain synaptic functions: excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), short-term potentiation (STP), short-term depression (STD), the transition from short-term plasticity to long-term plasticity, spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), the process of learning, forgetting, and relearning, and Pavlov associative memory under UV light stimulation. The successful simulation of the synaptic behavior of this device provides the possibility for biomaterials to realize neuromorphic computing.

Uniform multilevel switching and synaptic properties in RF-sputtered InGaZnO-based memristor treated with oxygen plasma.

Bipolar gradual resistive switching was investigated in ITO/InGaZnO/ITO resistive switching devices. Controlled intrinsic oxygen vacancy formation inside the switching layer enabled the establishment of a stable multilevel memory state, allowing for RESET voltage control and non-degradable data endurance. The ITO/InGaZnO interface governs the migration of oxygen ions and redox reactions within the switching layer. Voltage-stress-induced electron trapping and oxygen vacancy formation were observed before conductive filament electroforming. This device mimicked biological synapses, demonstrating short- and long-term potentiation and depression through electrical pulse sequences. Modulation of post-synaptic currents and pulse frequency-dependent short-term potentiation were successfully emulated in the InGaZnO-based artificial synapse. The ITO/InGaZnO/ITO memristor exhibited spike-amplitude-dependent plasticity, spike-rate-dependent plasticity, and potentiation-depression synaptic learning with low energy consumption, making it a promising candidate for large-scale integration.

Light-Stimulated IGZO Transistors with Tunable Synaptic Plasticity Based on Casein Electrolyte Electric Double Layer for Neuromorphic Systems.

In this study, optoelectronic synaptic transistors based on indium-gallium-zinc oxide (IGZO) with a casein electrolyte-based electric double layer (EDL) were examined. The casein electrolyte played a crucial role in modulating synaptic plasticity through an internal proton-induced EDL effect. Thus, important synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, and spike rate-dependent and spike number-dependent plasticity, were successfully implemented by utilizing the persistent photoconductivity effect of the IGZO channel stimulated by light. The synergy between the light stimulation and the EDL effect allowed the effective modulation of synaptic plasticity, enabling the control of memory levels, including the conversion of short-term memory to long-term memory. Furthermore, a Modified National Institute of Standards and Technology digit recognition simulation was performed using a three-layer artificial neural network model, achieving a high recognition rate of 90.5%. These results demonstrated a high application potential of the proposed optoelectronic synaptic transistors in neuromorphic visual systems.