Co-Stimuli-Driven 2D WSe2 Optoelectronic Synapses for Neuromorphic Computing.

All-in-one metal-oxide heterojunction artificial synapses for visual sensory and neuromorphic computing systems

This article demonstrates fabrication and testing of novel memory devices using indium sulfide (In2S3) as an active material. A wafer scalable process is used to develop nanostructured In2S3 for the mass production of optical memory devices. The developed devices have shown outstanding light-sensing capability as well as memory functionality. The In2S3 devices are highly responsive toward the 880 nm with higher responsivity and detectivity of 15.88 A/W and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.84\times 10^{{11}}$ </tex-math></inline-formula> Jones, respectively. In addition, these optical memory devices were able to mimic cognitive functionalities like high paired-pulse facilitation (PPF) index, long-term plasticity (LTP), and short-term plasticity (STP) compared to the state of the art. The optical memory devices have shown a higher PPF index of 227% and long-term memory (LTM) retention capability of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 10^{{3}}$ </tex-math></inline-formula> s. Devices were optically potentiated for broadband spectrum and reset using electrical stimulation. Moreover, In2S3-based devices have highly repeatable, scalable, stable, and reproducible characteristics at room temperature with a rise/fall time of 0.4/0.01 s. The fabricated optical memory devices can be deployed for future neuromorphic computing applications.

Since the introduction of a concept of memristor by Leon Chua in 1971 as the fourth fundamental passive component in electronic circuits, and its functional prototype introduced by HP Lab in 2008, research has increasingly concentrated on exploring its application ability in electronic circuits. First, the memristor principles have been exploited in various memory devices within a classical von Neumann computer architecture. The properties of memristor were found particularly suitable for resistive random-access memories (ReRAM).1 Contrary to the classical von Neumann architecture based on the binary logic, advanced methods like neuromorphic computing require an electronic element with continuously varying states depending on previous input signals. The input signal consists of a sequence of voltage spikes that stimulate the device, similarly as a neural synapse does in biological systems. These trains of spikes change the output state of the device continuously to a lower resistance state, mimicking a learning process. Having these functionalities, memristors are considered to be possible building blocks of brain-inspired neuromorphic computing and artificial neural networks.2 Memristors with neurosynaptic functionality take a continuity of resistance values with synaptic weights modulated by the number and frequency of homogeneous spikes. In order to mimic the neural synapse, memristors must exhibit analog properties including non-abrupt switching transitions, memory loss, continuously variable resistance states, and predictable response. We present such functionality on thin films of two kinds of organic materials:(i) A newly synthesized poly(methacrylamide) derivative with carbazole charge transporting group separated from the polymer backbone by an alkyl chain, was synthesized recently and found to behave as a bistable resistive memory.3 Thin films of the polymer, sandwiched between Al and ITO electrodes, exhibit rewritable flash memory behavior with bistable conductivity, with the set switching voltage ranging from 2 to 4.5 V, and the current ON/OFF ratio exceeding 100. The device demonstrates a remarkable lifetime and remains persistent for more than 104 seconds under the static voltage of 0.5 V. The main physical mechanisms driving the resistive switching have been attributed to the electric field-induced reorientation of heterocycles, which modulates charge transport, and trapping/detrapping of charges in localized states within the bulk of the polymer. Memory persistence is strengthened by the physical crosslinking caused by hydrogen bonds between amide and carbonyl groups in the side chains. Depending on the layer thickness, electrode material and applied voltage range the electrical characteristics can change from bistable to analog behavior showing memristive properties.(ii) Ditopic π-conjugated bis-terpyridine (tpy-) ligands form supramolecular structures in which the ligands are interconnected by complexation with metal ions. These molecules offer a low energy operation with improved reproducibility of memristive characteristics. They possess low-lying molecular energy levels and relatively narrow bandgap enabling the low-power consuming resistive switching and longer stability. We used a newly synthesized bis-terpyridine ligands based on 9-phenyl carbazole, Cbz, and its complex with cobalt Co2+ as an active layer of a memristor device. A two-terminal memristor architecture was employed, consisting of the ligand or its cobalt complex deposited on an ITO substrate, and aluminum, gold, or gallium as a top electrode. We show that Cbz as a memristor active layer exhibits an excellent nonvolatile bistable memory behavior, while Co-Cbz exhibits both electronic memory and synaptic plasticity. The ligand exhibited bi-stable conduction and non-volatile memory effect with persistence over 18 hours, while its cobalt complex displayed a synaptic effect characterized by subsequent potentiation and depression cycles. Voltage-induced modulation of synaptic weight was observed with a low applied voltage below 500 mV and short pulse duration below 20 ms. Pair pulse facilitation and pair pulse depression demonstrated significant synaptic weight changes, showing the cobalt complex ability to emulate biological neurons. The operational mechanism was attributed to a combination of the redox effect in the ligand and complex, and to the voltage-induced migration of perchlorate counter ions and subsequent redox processes, altering the metal-to-ligand charge transfer band and changing the conducting state of the active layer.Acknowledgments: The work was financially supported by the Czech Science Foundation, project 24-10384S, and MEYS of the Czech Republic, project LUAUS24032. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).Chang, T., Jo, S. H. & Lu, W. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5, 7669–7676 (2011).Panthi, Y.R, Pfleger, J., Vyprachticky, D., Pandey, A., Thottappali, M.A., Sedenkova, I., Konefal, M. & Foulger, S.H. Rewritable resistive memory effect in poly[N-(3-(9H-carbazol-9-yl)propyl)-methacrylamide] memristor. Mater. Chem. C 11, 17093 (2023).

Neuromorphic computing, an emerging computational paradigm, aims to overcome the bottlenecks of the traditional von Neumann architecture. Two-dimensional materials serve as ideal platforms for constructing artificial synaptic devices, yet existing devices based on these materials face challenges such as insufficient stability. Indium selenide (InSe), a two-dimensional semiconductor with unique properties, demonstrates significant potential in the field of neuromorphic devices, though its application research remains in the initial stage. This study presents an artificial synaptic device based on the InSe/Charge Trapping Layer (CTL)/h-BN heterojunction. By applying oxygen plasma treatment to h-BN to form a controllable charge-trapping layer, efficient regulation of carriers in the InSe channel is achieved. The device successfully emulates fundamental synaptic behaviors including paired-pulse facilitation and long-term potentiation/inhibition, exhibiting excellent reproducibility and stability. Through investigating the influence of electrical pulse parameters on synaptic weights, a structure–activity relationship between device performance and structural parameters is established. Experimental results show that the device features outstanding linearity and symmetry, realizing the simulation of key synaptic behaviors such as dynamic conversion between short-term and long-term plasticity. It possesses a high dynamic range ratio of 7.12 and robust multi-level conductance tuning capability, with stability verified through 64 pulse cycle tests. This research provides experimental evidence for understanding interfacial charge storage mechanisms, paves the way for developing high-performance neuromorphic computing devices, and holds broad application prospects in brain-inspired computing and artificial intelligence hardware.

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.

Optical artificial synapses possess several advantages, including high bandwidth, strong interference immunity, and ultra-fast signal transmission, overcoming the limitations of electrically stimulated synapses. Among various functional materials, 2D materials exhibit exceptional optical and electrical properties. By utilizing van der Waals heterostructures formed by these materials through rational design, synaptic devices can mimic the information perception ability of biological systems. This lays the foundation for low-energy artificial vision systems and neuromorphic computing. This study introduces an inhibitory artificial synapse based on photoelectric co-modulation of graphene/WSe2 van der Waals heterojunctions. By synergistically applying gate voltage and light pulses, we simulate memory and logic functions observed in the brain’s visual cortex. We achieve the construction of inhibitory synapses, enabling properties such as postsynaptic current response, short-term and long-term plasticity, and paired-pulse facilitation. Additionally, we accomplish the inverse recovery of device conductivity through separate gate voltage stimulation. Through bidirectional modulation of the artificial synaptic conductance, we construct an artificial hardware neural network that achieves 92.5% accuracy in recognizing handwritten digital images from the MNIST dataset. The network also has good recognition accuracy for handwritten digital images with different standard deviation Gaussian noise applied and other datasets. Furthermore, we successfully mimic the neural behavior of aversive learning for alcohol withdrawal in alcoholic patients using the device properties. The promising capabilities of artificial synapses constructed through electrical and optical synergistic modulation make them suitable for wearable electronics and artificial vision systems.

The emerging two-terminal memristor with a conductance-adjustable function under external stimulation is considered a strong candidate for use in artificial memory and electronic synapses. However, the stability, uniformity, and power consumption of memristors are still challenging in neuromorphic computing. Here an Au/SnSe/graphene/SiO2/Si memristor was fabricated, incorporating two-dimensional graphene with high thermal conductivity. The device not only exhibits excellent electrical characteristics (e.g., high stability, good uniformity and a high ROFF/RON ratio), but also can implement biological synaptic functions such as paired-pulse facilitation, short-term plasticity and long-term plasticity. Its set and reset power values can be as low as 16.7 and 2.3 nW, respectively. Meanwhile, the resistance switching mechanism for the device, which might be associated with the formation and rupture of a filamentary conducting path consisting of Sn vacancies, was confirmed by high-resolution transmission electron microscopy observations. The proposed device is an excellent candidate for use in high-density storage and low-power neuromorphic computing applications.

Surface ligand engineering of perovskite quantum dots for n-type and stretchable photosynaptic transistor with an ultralow energy consumption

In this work, a solution-processable oxide-based memtransistor is designed for neuromorphic computing, incorporating LiInSnO4 as the gate dielectric, SnO2 as the semiconducting channel, and Ag+-exchanged LiV3O8 as the resistive switching medium. The device demonstrates dual tunability in its channel conductance through both gate voltage and light modulation, enabling precise control over its switching characteristics. Operating at low voltages, the memtransistor achieves an LRS/HRS ratio of up to 103, with stable performance across 103 switching cycles, over 106 pulse cycles, and retention up to 105 seconds. The device effectively replicates essential synaptic functions such as paired-pulse facilitation and short- and long-term plasticity, with ultra-low energy consumption: 193 pJ (0.1 fJ μm-2) optically and 540 pJ (0.3 fJ μm-2) electrically. It also shows low non-linearity in potentiation/depression events, 0.49/3.47 (optical) and 0.03/5.67 (electrical), facilitating accurate synaptic weight modulation. Light-driven logic operations and cognitive functions, learning, forgetting, and relearning are successfully demonstrated, along with Pavlovian classical conditioning. Neural network simulations confirm 98% and 95% recognition accuracy for optical and electrical synapses, while autoencoder-based denoising and data reconstruction further validate the applicability of the device in brain-inspired computing.

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.

Neuromorphic computing systems convert multimodal signals to electrical responses for artificial intelligence recognition. Energy is consumed during both the response enhancement and depression, making the systems suffer from high energy consumption. This study presents a neuromorphic computing pathway based on supercapacitors. MXene Ti₃C₂Tx supercapacitors are fabricated and convert current stimuli to voltage responses. The response enhancement and depression are tunable through adjusting charging and discharging current stimuli, thus exhibiting synaptic plasticity. Typical synaptic behaviors are demonstrated, including short‐term memory, long‐term memory, paired‐pulse facilitation, and learning experience. Next, the voltage responses are used to recognize Braille numbers represented by 3 × 4 arrays. A charging/discharging current pulse train representing each Braille array is applied to the supercapacitor. The voltage responses are collected and converted to 12‐pixel greyscale images. Once the images representing Braille numbers 0–9 are input into artificial neural networks and deep diffraction neural networks, 100% accuracy can be achieved for recognizing the ten numbers. Because energy is stored during response enhancement in the supercapacitor and released once the response declines, this research demonstrates the potential applications of energy storage devices in neuromorphic computing, providing an innovative way to develop energy‐efficient brain‐like computing systems.

&lt;p&gt;The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and Neuromorphic Computing. In particular, the memory demands imposed by the vast number of synapses in brain-scale network simulations constitute a major obstacle. Limiting the number resolution of synaptic weights appears to be a natural strategy to reduce memory and compute load. In this study, we investigate the effects of a limited synaptic-weight resolution on the dynamics of recurrent spiking neuronal networks resembling local cortical circuits and develop strategies for minimizing deviations from the dynamics of networks with high-resolution synaptic weights. We mimic the effect of a limited synaptic weight resolution by replacing normally distributed synaptic weights with weights drawn from a discrete distribution, and compare the resulting statistics characterizing firing rates, spike-train irregularity, and correlation coefficients with the reference solution. We show that a naive discretization of synaptic weights generally leads to a distortion of the spike-train statistics. If the weights are discretized such that the mean and the variance of the total synaptic input currents are preserved, the firing statistics remain unaffected for the types of networks considered in this study. For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution. We conclude that even for simple networks with non-plastic neurons and synapses, a discretization of synaptic weights can lead to substantial deviations in the firing statistics unless the discretization is performed with care and guided by a rigorous validation process. For the network model used in this study, the synaptic weights can be replaced by low-resolution weights without affecting its macroscopic dynamical characteristics, thereby saving substantial amounts of memory.&lt;/p&gt;

Artificial synaptic simulating pain-perceptual nociceptor and brain-inspired computing based on Au/Bi3.2La0.8Ti3O12/ITO memristor

Current artificial neuromorphic systems are mainly constructed using solid‐state optoelectronic synaptic devices, limiting their potential artificial intelligence applications in liquid medium. Here, colloidal CuInGaSe/ZnSe quantum dots (QDs) with environment‐benign feature and broad visible light absorption are prepared to fabricate a photoelectrochemical (PEC)‐type optoelectronic synaptic device operating in aqueous electrolyte, enabling unique biological synaptic behaviors including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP) and learning‐forgetting‐relearning process when simulated by optical pulses with various wavelength, pulse number, frequency, power density, and applied voltage. These results demonstrate the feasibility of building PEC‐type optoelectronic synapses using colloidal QDs, paving the way to realize available optoelectronic synaptic devices for prospective underwater artificial intelligence technology.

Neuromorphic computing has the potential to address the inherent limitations of conventional integrated circuit technology, ranging from perception, pattern recognition, to memory and decision-making ( Acc. Chem. Res. 2019, 52 (4), 964-974) ( Nature 2004, 431 (7010), 796-803) ( Nat. Nanotechnol. 2013, 8 (1), 13-24). Despite their low power consumption ( Nano Lett. 2016, 16 (11), 6724-6732), traditional two-terminal memristors can perform only a single function while lacking heterosynaptic plasticity ( Nanotechnology 2013, 24 (38), 382001). Inspired by the unconditioned reflex, multiterminal memristive transistors (memtransistor) were developed to realize complex functions, such as multiterminal modulation and heterosynaptic plasticity ( Nature 2018, 554, (7693), 500-504). Here we combine a hybrid metal halide perovskite with an organic conjugated polymer to form heterojunction transistors that are responsive to both electrical and optical stimuli. We show that the synergistic effects of photoinduced ion migration in the perovskite and electronic transport in the polymer layers can be exploited to realize memristive functions. The device combines reversible, nonvolatile conductance modulation with large switching current ratios, high endurance, and long retention times. Using in situ scanning Kelvin probe microscopy and variable-temperature charge transport measurement, we correlate the collective effects of bias-induced and photoinduced ion migration with the heterosynaptic behavior observed in this hybrid memtransistor. The hybrid heterojunction channel concept is expected to be applicable to other material combinations making it a promising platform for deployment in innovative neuromorphic devices of the future.