Memristive Synapses with Photoelectric Plasticity Realized in ZnO1-x/AlOy Heterojunction

A Plasmonic Optoelectronic Resistive Random-Access Memory for In-Sensor Color Image Cryptography

The optoelectronic resistive random-access memory (RRAM) with the integrated function of perception, storage and intrinsic randomness displays promising applications in the hardware level in-sensor image cryptography. In this work, 2D hexagonal boron nitride based optoelectronic RRAM is fabricated with semitransparent noble metal (Ag or Au) as top electrodes, which can simultaneous capture color image and generate physically unclonable function (PUF) key for in-sensor color image cryptography. Surface plasmons of noble metals enable the strong light absorption to realize an efficient modulation of filament growth at nanoscale. Resistive switching curves show that the optical stimuli can impede the filament aggregation and promote the filament annihilation, which originates from photothermal effects and photogenerated hot electrons in localized surface plasmon resonance of noble metals. By selecting noble metals, the optoelectronic RRAM array can respond to distinct wavelengths and mimic the biological dichromatic cone cells to perform the color perception. Due to the intrinsic and high-quality randomness, the optoelectronic RRAM can produce a PUF key in every exposure cycle, which can be applied in the reconfigurable cryptography. The findings demonstrate an effective strategy to build optoelectronic RRAM for in-sensor color image cryptography applications.

VO2/MoO3 Heterojunctions Artificial Optoelectronic Synapse Devices for Near-Infrared Optical Communication

Artificial optoelectronic synapses (OES) have attracted extensive attention in brain-inspired information processing and neuromorphic computing. However, OES at near-infrared wavelengths have rarely been reported, seriously limiting the application in modern optical communication. Herein, high-performance near-infrared OES devices based on VO2/MoO3 heterojunctions are presented. The textured MoO3 films are deposited on the sputtered VO2 film by using the glancing-angle deposition technique to form a heterojunction device. Through tuning the oxygen defects in the VO2 film, the fabricated VO2/MoO3 heterojunction exhibits versatile electrical synaptic functions. Benefiting from the highly efficient light harvesting and the unique interface effect, the photonic synaptic characteristics, mainly including the short/long-term plasticity and learning experience behavior are successfully realized at the O (1342 nm) and C (1550 nm) optical communication wavebands. Moreover, a single OES device can output messages accurately by converting light signals of the Morse code to distinct synaptic currents. More importantly, a 3 × 3 artificial OES array is constructed to demonstrate the impressive image perceiving and learning capabilities. This work not only indicates the feasibility of defect and interface engineering in modulating the synaptic plasticity of OES devices, but also provides effective strategies to develop advanced artificial neuromorphic visual systems for next-generation optical communication systems.

Optoelectronic Synapse Behaviors in Tb3+ and Al3+ Co-Doped CaSnO3 with Long-Persistent Luminescence

Neuromorphic computation draws inspiration from the remarkable features of the human brain including low energy consumption, parallelism, adaptivity, cognitive functions, and learning ability. These qualities hold the promise of unlocking groundbreaking computational techniques that surpass the limitations of traditional computing systems. This paper reports a remarkable photo-synaptic behavior in the field of rare earth ion-doped luminescent oxides by using long-persistent luminescence (LPL). This system utilizes electron trap states to regulate the synaptic behavior, operating through a fundamentally different mechanism from that of electronic-based synaptic devices. To realize this strategy, Tb3+ doped CaSnO3, which shows a significant LPL property under UV-light excitation, is prepared. The luminescent system shows key neuromorphic characteristics such as paired-pulse facilitation, pulse-number/timing dependent potentiation, and pulse-number/timing dependent short- to long-term plasticity transition, which are required for realizing synaptic devices. This feature expands the way for advanced neuromorphic technologies employing light stimuli.

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices, opening numerous opportunities across countless domains, including personalized healthcare and advanced robotics. Leveraging 3D integration, edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption. Here, we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications, including electroencephalogram (EEG)-based seizure prediction, electromyography (EMG)-based gesture recognition, and electrocardiogram (ECG)-based arrhythmia detection. With experiments on three biomedical datasets, we observe the classification accuracy improvement for the pretrained model with 2.93% on EEG, 4.90% on ECG, and 7.92% on EMG, respectively. The optical programming property of the device enables an ultra-low power (2.8 × 10-13 J) fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios. Moreover, the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions, making it promising for neuromorphic vision application. To display the benefits of these intricate synaptic properties, a 5 × 5 optoelectronic synapse array is developed, effectively simulating human visual perception and memory functions. The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

Improvement of volatile switching in scaled silicon nanofin memristor for high performance and efficient reservoir computing

Conductive-bridge random access memory can be used as a physical reservoir for temporal learning in reservoir computing owing to its volatile nature. Herein, a scaled Cu/HfOx/n+-Si memristor was fabricated and characterized for reservoir computing. The scaled, silicon nanofin bottom electrode formation is verified by scanning electron and transmission electron microscopy. The scaled device shows better cycle-to-cycle switching variability characteristics compared with those of large-sized cells. In addition, synaptic characteristics such as conductance changes due to pulses, paired-pulse facilitation, and excitatory postsynaptic currents are confirmed in the scaled memristor. High-pattern accuracy is demonstrated by deep neural networks applied in neuromorphic systems in conjunction with the use of the Modified National Institute of Standards and Technology database. Furthermore, a reservoir computing system is introduced with six different states attained by adjusting the amplitude of the input pulse. Finally, high-performance and efficient volatile reservoir computing in the scaled device is demonstrated by conductance control and system-level reservoir computing simulations.

Tunable Negative and Positive Photoconductance in Van Der Waals Heterostructure for Image Preprocessing

The processing of visual information occurs mainly in the retina, and the retinal preprocessing function greatly improves the transmission quality and efficiency of visual information. The artificial retina system provides a promising path to efficient image processing. Here, graphene/InSe/h-BN heterogeneous structure is proposed, which exhibits negative and positive photoconductance (NPC and PPC) effects by altering the strength of a single wavelength laser. Moreover, a modified theoretical model is presented based on the power-dependent photoconductivity effect of laser:I ph = - mP α 1 + nP α 2 ${\rm I}_{\rm ph}\,=\,-{\rm mP}^{\alpha _{1}} + {\rm nP}^{\alpha _{2}}$ , which can reveal the internal physical mechanism of negative/positive photoconductance effects. The present 2D structure design allows the field effect transistor (FET) to exhibit excellent photoelectric performance (RNPC = 1.1× 104 AW-1, RPPC = 13 AW-1) and performance stability. Especially, the retinal pretreatment process is successfully simulated based on the negative and positive photoconductive effects. Moreover, the pulse signal input improves the device responsivity by 167%, and the transmission quality and efficiency of the visual signal can also be enhanced. This work provides a new design idea and direction for the construction of artificial vision, and lay a foundation for the integration of the next generation of optoelectronic devices.

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.

Imitating Synapse Behavior: Exploiting Off-Current in TPBi-Doped Small Molecule Phototransistors for Broadband Wavelength Recognition

Phototransistors have gained significant attention in diverse applications such as photodetectors, image sensors, and neuromorphic devices due to their ability to control electrical characteristics through photoresponse. The choice of photoactive materials in phototransistor research significantly impacts its development. In this study, we propose a novel device that emulates artificial synaptic behavior by leveraging the off-current of a phototransistor. We utilize a p-type organic semiconductor, dinaphtho[2,3-b:2',3'- f]thieno[3,2-b]thiophene (DNTT), as the channel material and dope it with the organic semiconductor 2,2',2″-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole) (TPBi) on the DNTT transistor. Under light illumination, the general DNTT transistor shows no change in off-current, except at 400 nm wavelength, whereas the TPBi-doped DNTT phototransistor exhibits increased off-current across all wavelength bands. Notably, DNTT phototransistors demonstrate broad photoresponse characteristics in the wavelength range of 400-1000 nm. We successfully simulate artificial synaptic behavior by differentiating the level of off-current and achieving a recognition rate of over 70% across all wavelength bands.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

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.

Optically driven intelligent computing with ZnO memristor

Artificial vision is crucial for most artificial intelligence applications. Conventional artificial visual systems have been facing challenges in terms of real-time information processing due to the physical separation of sensors, memories, and processors, which results in the production of a large amount of redundant data as well as the data conversion and transfer between these three components consuming most of the time and energy. Emergent optoelectronic memristors with the ability to realize integrated sensing-computing-memory (ISCM) are key candidates for solving such challenges and therefore attract increasing attention. At present, the memristive ISCM devices can only perform primary-level computing with external light signals due to the fact that only monotonic increase of memconductance upon light irradiation is achieved in most of these devices. Here, we propose an all-optically controlled memristive ISCM device based on a simple structure of Au/ZnO/Pt with the ZnO thin film sputtered at pure Ar atmosphere. This device can perform advanced computing tasks such as nonvolatile neuromorphic computing and complete Boolean logic functions only by light irradiation, owing to its ability to reversibly tune the memconductance with light. Moreover, the device shows excellent operation stability ascribed to a purely electronic memconductance tuning mechanism. Hence, this study is an important step towards the next generation of artificial visual systems.

Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction

Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.

Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation

In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.

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

Metal halide perovskite based materials have emerged over the past few decades as remarkable solution-processable optoelectronic materials with many intriguing properties and potential applications. These emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier-diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, recent progress toward memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality is reviewed.

Reversible Photomodulation of Two-Dimensional Electron Gas in LaAlO3/SrTiO3 Heterostructures

Long-lived photoinduced conductance changes in LaAlO₃/SrTiO₃ (LAO/STO) heterostructures enable their use in optoelectronic memory applications. However, it remains challenging to quench the persistent photoconductivity (PPC) instantly and reproducibly, which limits the reversible optoelectronic switching. Herein, we demonstrate a reversible photomodulation of two-dimensional electron gas (2DEG) in LAO/STO heterostructures with high reproducibility. By irradiating UV pulses, the 2DEG at the LAO/STO interface is gradually transformed to the PPC state. Notably, the PPC can be completely removed by water treatment when two key requirements are met: (1) the moderate oxygen deficiency in STO and (2) the minimal band edge fluctuation at the interface. Through our X-ray photoelectron spectroscopy and electrical noise analysis, we reveal that the reproducible change in the conductivity of 2DEG is directly attributed to the surface-driven electron relaxation in the STO. Our results provide a stepping-stone toward developing optically tunable memristive devices based on oxide 2DEG systems.

Neotype kuramite optoelectronic memristor for bio-synaptic plasticity simulations

Memristive devices with both electrically and optically induced synaptic dynamic behaviors will be crucial to the accomplishment of brain-inspired neuromorphic computing systems, in which the resistive materials and device architectures are two of the most important cornerstones, but still under challenge. Herein, kuramite Cu3SnS4 is newly introduced into poly-methacrylate as the switching medium to construct memristive devices, and the expected high-performance bio-mimicry of diverse optoelectronic synaptic plasticity is demonstrated. In addition to the excellent basic performances, such as stable bipolar resistive switching with On/Off ratio of ∼486, Set/Reset voltage of ∼-0.88/+0.96 V, and good retention feature of up to 104 s, the new designs of memristors possess not only the multi-level controllable resistive-switching memory property but also the capability of mimicking optoelectronic synaptic plasticity, including electrically and visible/near-infrared light-induced excitatory postsynaptic currents, short-/long-term memory, spike-timing-dependent plasticity, long-term plasticity/depression, short-term plasticity, paired-pulse facilitation, and "learning-forgetting-learning" behavior as well. Predictably, as a new class of switching medium material, such proposed kuramite-based artificial optoelectronic synaptic device has great potential to be applied to construct neuromorphic architectures in simulating human brain functions.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

All oxide based flexible multi-folded invisible synapse as vision photo-receptor

All oxide-based transparent flexible memristor is prioritized for the potential application in artificially simulated biological optoelectronic synaptic devices. SnOx memristor with HfOx layer is found to enable a significant effect on synaptic properties. The memristor exhibits good reliability with long retention, 104 s, and high endurance, 104 cycles. The optimized 6 nm thick HfOx layer in SnOx-based memristor possesses the excellent synaptic properties of stable 350 epochs training, multi-level conductance (MLC) behaviour, and the nonlinearity of 1.53 and 1.46 for long-term potentiation and depression, respectively, and faster image recognition accuracy of 100% after 23 iterations. The maximum weight changes of -73.12 and 79.91% for the potentiation and depression of the synaptic device, respectively, are observed from the spike-timing-dependent plasticity (STDP) characteristics making it suitable for biological applications. The flexibility of the device on the PEN substrate is confirmed by the acceptable change of nonlinearities up to 4 mm bending. Such a synaptic device is expected to be used as a vision photo-receptor.

An artificial optoelectronic synapse based on MoOxfilm

Artificial optoelectronic synapses have the advantages of large bandwidth, low power consumption and low crosstalk, and are considered to be the basic building blocks of neuromorphic computing. In this paper, a two-terminal optoelectronic synaptic device with ITO-MoO_x-Pt structure is prepared by magnetron sputtering. The performance of resistive switching (RS) and the photo plastic properties of the device are analyzed and demonstrated. Electrical characterization tests show that the device has a resistive HRS/LRS ratio of about 90, stable endurance, and retention characteristics of more than 10⁴s (85 °C). The physical mechanism of the device is elucidated by a conducting filament composed of oxygen vacancies. Furthermore, the function of various synaptic neural morphologies is successfully mimicked using UV light as the stimulation source. Including short-term/long-term memory, paired-pulse facilitation, the transition from short-term to long-term memory, and 'learning-experience' behavior. Integrated optical sensing and electronic data storage devices have great potential for future artificial intelligence, which will facilitate the rapid development of retina-like visual sensors and low-power neuromorphic systems.

Non-Volatile Memory and Synaptic Characteristics of TiN/CeOx/Pt RRAM Devices

In this study, we investigate the synaptic characteristics and the non-volatile memory characteristics of TiN/CeOx/Pt RRAM devices for a neuromorphic system. The thickness and chemical properties of the CeOx are confirmed through TEM, EDS, and XPS analysis. A lot of oxygen vacancies (ions) in CeOx film enhance resistive switching. The stable bipolar resistive switching characteristics, endurance cycling (>100 cycles), and non-volatile properties in the retention test (>10,000 s) are assessed through DC sweep. The filamentary switching model and Schottky emission-based conduction model are presented for TiN/CeOx/Pt RRAM devices in the LRS and HRS. The compliance current (1~5 mA) and reset stop voltage (−1.3~−2.2 V) are used in the set and reset processes, respectively, to implement multi-level cell (MLC) in DC sweep mode. Based on neural activity, a neuromorphic system is performed by electrical stimulation. Accordingly, the pulse responses achieve longer endurance cycling (>10,000 cycles), MLC (potentiation and depression), spike-timing dependent plasticity (STDP), and excitatory postsynaptic current (EPSC) to mimic synapse using TiN/CeOx/Pt RRAM devices.

CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing

Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO₂/TiO₂/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 10⁴ s, and endurance of 10⁶ cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application.

Optical and optoelectronic neuromorphic devices based on emerging memory technologies

As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.

'Stateful' threshold switching for neuromorphic learning

Memristors have promising prospects in developing neuromorphic chips that parallel the brain-level power efficiency and brain-like computational functions. However, the limited available ON/OFF states and high switching voltage in conventional resistive switching (RS) constrain its practical and flexible implementations to emulate biological synaptic functions with low power consumption. We present 'stateful' threshold switching (TS) within the millivoltage range depending on the resistive states of RS, which originates from the charging/discharging parasitic elements of a memristive circuit. Fundamental neuromorphic learning can be facilely implemented based on a single memristor by utilizing four resistive states in 'stateful' TS. Besides the metaplasticity of synaptic learning-forgetting behaviors, multifunctional associative learning, involving acquisition, extinction, recovery, generalization and protective inhibition, was realized with nonpolar operation and power consumption of 5.71 pW. The featured 'stateful' TS with flexible tunability, enriched states, and ultralow operating voltage will provide new directions toward a massive storage unit and bio-inspired neuromorphic system.

Plasmonic Optoelectronic Memristor Enabling Fully Light-Modulated Synaptic Plasticity for Neuromorphic Vision

Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficient neuromorphic vision. In this work, the authors develop a plasmonic optoelectronic memristor that relies on the effects of localized surface plasmon resonance (LSPR) and optical excitation in an Ag-TiO2 nanocomposite film. Fully light-induced synaptic plasticity (e.g., potentiation and depression) under visible and ultraviolet light stimulations is demonstrated, which enables the functional combination of visual sensing and low-level image pre-processing (including contrast enhancement and noise reduction) in a single device. Furthermore, the light-gated and electrically-driven synaptic plasticity can be performed in the same device, in which the spike-timing-dependent plasticity (STDP) learning functions can be reversibly modulated by visible and ultraviolet light illuminations. Thereby, the high-level image processing function, i.e., image recognition, can also be performed in this memristor, whose recognition rate and accuracy are obviously enhanced as a result of image pre-processing and light-gated STDP enhancement. Experimental analysis shows that the memristive switching mechanism under optical stimulation can be attributed to the oxidation/reduction of Ag nanoparticles due to the effects of LSPR and optical excitation. The authors' work proposes a new type of plasmonic optoelectronic memristor with fully light-modulated capability that may promote the future development of efficient neuromorphic vision.

Fabrication of an amorphous metal oxide/p-BiVO4 photocathode: understanding the role of entropy for reducing nitrate to ammonia

Entropy regulation makes an amorphous metal oxide/p-BiVO4 heterostructure a desirable catalyst for the NO3− reduction reaction in a photoelectrochemical system.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

Free-Standing Multilayer Molybdenum Disulfide Memristor for Brain-Inspired Neuromorphic Applications

Recently, atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDs) have attracted great interest in electronic and opto-electronic devices for high-integration-density applications such as data storage due to their small vertical dimension and high data storage capability. Here, we report a memristor based on free-standing multilayer molybdenum disulfide (MoS₂) with a high current on/off ratio of ∼10³ and a stable retention for at least 3000 s. Through light modulation of the carrier density in the suspended MoS₂ channel, the on/off ratio can be further increased to ∼10⁵. Moreover, the essential photosynaptic functions with short- and long-term memory (STM and LTM) behaviors are successfully mimicked by such devices. These results also indicate that STM can be transferred to LTM by increasing the light stimuli power, pulse duration, and number of pulses. The electrical measurements performed under vacuum and ambient air conditions propose that the observed resistive switching is due to adsorbed oxygen and water molecules on both sides of the MoS₂ channel. Thus, our free-standing 2D multilayer MoS₂-based memristors propose a simple approach for fabrication of a low-power-consumption and reliable resistive switching device for neuromorphic applications.

Optoelectronic Artificial Synapses Based on Two-Dimensional Transitional-Metal Trichalcogenide

The memristor is a foundational device for an artificial synapse, which is essential to realize next-generation neuromorphic computing. Herein, an optoelectronic memristor based on a two-dimensional (2D) transitional-metal trichalcogenide (TMTC) is designed and demonstrated. Owing to the excellent optical and electrical characteristics of titanium trisulfide (TiS₃), the memristor exhibits stable bipolar resistance switching (RS) as a result of the controllable formation and rupturing of the conductive aluminum filaments. Multilevel storage is realized with light of multiple wavelengths between 400 and 808 nm, and the synaptic properties such as conduction modulation and spiking timing-dependent plasticity (STDP) are achieved. On the basis of the photonic potentiation and electrical habitual ability, Pavlovian-associative learning is successfully established on this TiS₃-based artificial synapse. All these results reveal the large potential of 2D TMTCs in artificial neuromorphic chips.

Configurable switching behavior in polymer-based resistive memories by adopting unique electrode/electrolyte arrangement

The difference in resistive switching characteristics by modifying the device configuration provides a unique operating principle, which is essential for both fundamental studies and the development of future memory devices. Here, we demonstrate the poly(methyl methacrylate) (PMMA)-based resistive switching characteristics using four different combinations of electrode/electrolyte arrangement in the device geometry. From the current-voltage (I-V) measurements, all the PMMA-based devices revealed nonvolatile memory behavior with a higher ON/OFF resistance ratio (∼105-107). Significantly, the current conduction in the filament and resistive switching behavior depend majorly on the presence of Al electrode and electrochemically active silver (Ag) element in the PMMA matrix. A trap-controlled space charge limited conduction (SCLC) mechanism constitutes the resistive switching in the Al/PMMA/Al device, whereas the current conduction governed by ohmic behavior influences the resistive switching in the Ag-including devices. The depth-profiling X-ray photoelectron spectroscopy (XPS) study evidences the conducting filament formation processes in the PMMA-based devices. These results with different conduction mechanisms provide further insights into the understanding of the resistive switching behavior in the polymer-based devices by simply rearranging the device configuration.

Memristive Artificial Synapses for Neuromorphic Computing

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

High photosensitivity light-controlled planar ZnO artificial synapse for neuromorphic computing

A light-controlled artificial synapse, which mimics the human brain has been considered to be one of the ideal candidates for the fundamental physical architecture of a neuromorphic computing system owing to the possible abilities of high bandwidth and low power calculation. However, the low photosensitivity of synapse devices can affect the accuracy of recognition and classification in neuromorphic computing tasks. In this work, a planar light-controlled artificial synapse having high photosensitivity (Ion/Ioff > 1000) with a high photocurrent and a low dark current is realized based on a ZnO thin film grown by radiofrequency sputtering. The synaptic functions of the human brain such as sensory memory, short-term memory, long-term memory, duration-time-dependent-plasticity, light-intensity-dependent-plasticity, learning-experience behavior, neural facilitation, and spike-timing-dependent plasticity are successfully emulated using persistent photoconductivity characteristic of a ZnO thin film. Furthermore, the high classification accuracy of 90%, 92%, and 86% after 40 epochs for file type datasets, small digits, and large digit is realized with a three-layer neural network based on backpropagation where the numerical weights in the network layer are mapped directly to the conductance states of the experimental synapse devices. Finally, characterization and analysis reveal that oxygen vacancy defects and chemisorbed oxygen on the surface of the ZnO film are the main factors that determine the performance of the device.

Highly Sensitive Artificial Visual Array Using Transistors Based on Porphyrins and Semiconductors

Artificial visual systems with image sensing and storage functions have considerable potential in the field of artificial intelligence. Light-stimulated synaptic devices can be applied for neuromorphic computing to build artificial visual systems. Here, optoelectronic synaptic transistors based on 5,15-(2-hydroxyphenyl)-10,20-(4-nitrophenyl)porphyrin (TPP) and dinaphtho[2,3-b:2',3'-f ]thieno[3,2-b]thiophene (DNTT) are demonstrated. By utilizing stable TPP with high light absorption, the number of photogenerated carriers in the transport layer can be increased significantly. The devices exhibit high photosensitivity and tunable synaptic plasticity. The synaptic weight can be effectively modulated by the intensity, width, and wavelength of the light signals. Due to the high light absorption of TPP, an ultrasensitive artificial visual array based on these devices is developed, which can detect weak light signals as low as 1 µW cm^-2 . Low-voltage operation is further demonstrated. Even with applied voltages as low as -70 µV, the devices can still show obvious responses, leading to an ultralow energy consumption of 1.4 fJ. The devices successfully demonstrate image sensing and storage functions, which can accurately identify visual information. In addition, the devices can preprocess information and achieve noise reduction. The excellent synaptic behavior of the TPP-based electronics suggests their good potential in the development of new intelligent visual systems.

Optical Axons for Electro-Optical Neural Networks

Recently, neuromorphic sensors, which convert analogue signals to spiking frequencies, have ‎been reported for neurorobotics. In bio-inspired systems these sensors are connected to the main neural unit to perform ‎post-processing of the sensor data. The performance of spiking neural networks has been ‎improved using optical synapses, which offer parallel communications between the distanced ‎neural areas but are sensitive to the intensity variations of the optical signal. For systems with ‎several neuromorphic sensors, which are connected optically to the main unit, the use of ‎optical synapses is not an advantage. To address this, in this paper we propose and ‎experimentally verify optical axons with synapses activated optically using digital signals. The ‎synaptic weights are encoded by the energy of the stimuli, which are then optically transmitted ‎independently. We show that the optical intensity fluctuations and link's misalignment result ‎in delay in activation of the synapses. For the proposed optical axon, we have demonstrated line of ‎sight transmission over a maximum link length of 190 cm with a delay of 8 μs. Furthermore, we ‎show the axon delay as a function of the illuminance using a fitted model for which the root mean square error (RMS) ‎similarity is 0.95.

Atomic Layer Deposition of Ga2O3/ZnO Composite Films for High-Performance Forming-Free Resistive Switching Memory

The resistive switching behavior in resistive random access memories (RRAMs) using atomic-layer-deposited Ga₂O₃/ZnO composite film as the dielectric was investigated. By alternatively atomic-layer-depositing Ga₂O₃ and ZnO with different thickness, we can accurately control the oxygen vacancy concentration. When regulating ZnO to ∼31%, the RRAMs exhibit a forming-free property as well as outstanding performance, including the ratio of a high resistance state to the low resistance state of 1000, retention time of more than 1 × 10⁴ s, and the endurance of 100. By preparing RRAMs of different Zn concentration, we carried out a comparative study and explored the physical origin for the forming-free property as well as good performance. Finally, a unified model is proposed to account for the resistive switching and the current conduction mechanism, providing meaningful insights in the development of high-quality and forming-free RRAMs for future memory and neuromorphic applications.

Synergistic Improvement of Long-Term Plasticity in Photonic Synapses Using Ferroelectric Polarization in Hafnia-Based Oxide-Semiconductor Transistors

A number of synapse devices have been intensively studied for the neuromorphic system which is the next-generation energy-efficient computing method. Among these various types of synapse devices, photonic synapse devices recently attracted significant attention. In particular, the photonic synapse devices using persistent photoconductivity (PPC) phenomena in oxide semiconductors are receiving much attention due to the similarity between relaxation characteristics of PPC phenomena and Ca²⁺ dynamics of biological synapses. However, these devices have limitations in its controllability of the relaxation characteristics of PPC behaviors. To utilize the oxide semiconductor as photonic synapse devices, relaxation behavior needs to be accurately controlled. In this study, a photonic synapse device with controlled relaxation characteristics by using an oxide semiconductor and a ferroelectric layer is demonstrated. This device exploits the PPC characteristics to demonstrate synaptic functions including short-term plasticity, paired-pulse facilitation (PPF), and long-term plasticity (LTP). The relaxation properties are controlled by the polarization of the ferroelectric layer, and this polarization is used to control the amount by which the conductance levels increase during PPF operation and to enhance LTP characteristics. This study provides an important step toward the development of photonic synapses with tunable synaptic functions.

Flexible full two-dimensional memristive synapses of graphene/WSe2−xOy/graphene

van der Waals heterostructures realized by stacking different two-dimensional materials offer the possibility to design new devices with atomic-level precision.

An electro-photo-sensitive synaptic transistor for edge neuromorphic visual systems

The practical application of optoelectronic artificial synapses in neuromorphic visual systems is still hindered by their limited functionality, reliability and the challenge of mass production. Here, an electro-photo-sensitive synapse based on a highly reliable amorphous InGaZnO thin-film transistor is demonstrated. Not only does the synapse respond to electrical voltage spikes due to charge trapping/detrapping, but also the weight is modified directly by persistent photocurrent effects under UV-light stimulation. Representative forms of synaptic plasticity, including inhibitory and excitatory postsynaptic currents, frequency-dependent characteristics, short-term to long-term plasticity transitions, and summation effects, are successfully demonstrated. In particular, optoelectronic synergetic modulation leads to reconfigurable excitatory and inhibitory synaptic behaviors, which provides a promising way to achieve the homeostatic regulation of synaptic weights. Moreover, the analogue channel conductance with 100 states is used as the weight update rule to perform MNIST handwritten digit recognition, using system-level LeNet-5 convolutional neural network simulations. The network shows a high recognition accuracy of 95.99% and good tolerance to noisy input patterns. This study highlights the commercial potential of mature optoelectronic InGaZnO transistor technology in edge neuromorphic systems.

Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm

The fundamental unit of the nervous system is a synapse, which is involved in transmitting information between neurons as well as learning, memory, and forgetting processes. Two-terminal memristors can fulfil most of these requirements; however, their poor dynamic changes in resistance to input electric stimuli remain an obstacle, which must be improved for accurate and quick information processing. Herein, we demonstrate the synaptic properties of ZnO-based memristors, which were significantly enhanced (∼340 times) by geometrical modulation due to the localized electric field enhancement. Specifically, by inserting Ag-nanowires and Ag-dots into the ZnO/Si interface, the resistive switching could be controlled from a digital to analog mode. A finite element simulation revealed that the presence of Ag could enhance the localized electric field, which in turn improved the migration of ionic species. Further, the device showed a variety of comprehensive synaptic functions, for instance, paired-pulse facilitation and transformation from short-term plasticity to long-term plasticity, including the Pavlovian associative learning process in a human brain. Our study presents a novel architecture to enhance the synaptic sensitivity, and its uses in practical applications, including the artificial learning algorithm.

Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for Neuromorphic Computing

The accumulation and extrusion of Ca²⁺ ions in the pre- and post-synaptic terminals play crucial roles in initiating short- and long-term plasticity (STP and LTP) in biological synapses, respectively. Mimicking these synaptic behaviors by electronic devices represents a vital step toward realization of neuromorphic computing. However, the majority of reported synaptic devices usually focus on the emulation of qualitatively synaptic behaviors; devices that can truly emulate the physical behavior of the synaptic Ca²⁺ ion dynamics in STP and LTP are rarely reported. In this work, Ag/Ag:Ta₂O₅/Pt self-doping memristors were developed to equivalently emulate the Ca²⁺ ion dynamics of biological synapses. With conductive filaments from double sources, these memristors produced unique double-switching behavior under voltage sweeps and demonstrated several essential synaptic behaviors under pulse stimuli, including STP, LTP, STP to LTP transition, and spike-rate-dependent plasticity. Experimental results and nanoparticle dynamic simulations both showed that Ag atoms from double sources could mimic Ca²⁺ dynamics in the pre- and post-synaptic terminals under stimuli. A perceptron network with an STP to LTP transition layer based on the self-doping memristors was also introduced and evaluated; simulations showed that this network could solve noisy figure recognition tasks efficiently. All of these results indicate that the self-doping memristors are promising components for future hardware creation of neuromorphic systems and emulate the characteristics of the brain.

A Highly Transparent Artificial Photonic Nociceptor

A nociceptor is an essential element in the human body, alerting us to potential damage from extremes in temperature, pressure, etc. Realizing nociceptive behavior in an electronics device remains a central issue for researchers, designing neuromorphic devices. This study proposes and demonstrates an all-oxide-based highly transparent ultraviolet-triggered artificial nociceptor, which responds in a very similar way to the human eye. The device shows a high transmittance (>65%) and very low absorbance in the visible region. The current-voltage characteristics show loop opening, which is attributed to the charge trapping/detrapping. Further, the ultraviolet-stimuli-induced versatile criteria of a nociceptor such as a threshold, relaxation, allodynia, and hyperalgesia are demonstrated under self-biased condition, providing an energy-efficient approach for the neuromorphic device operation. The reported optically controlled features open a new avenue for the development of transparent optoelectronic nociceptors, artificial eyes, and memory storage applications.

An Oxide Schottky Junction Artificial Optoelectronic Synapse

The rapid development of artificial intelligence techniques and future advanced robot systems sparks emergent demand on the accurate perception and understanding of the external environments via visual sensing systems that can co-locate the self-adaptive detecting, processing, and memorizing of optical signals. In this contribution, a simple indium-tin oxide/Nb-doped SrTiO₃ (ITO/Nb:SrTiO₃) heterojunction artificial optoelectronic synapse is proposed and demonstrated. Through the light and electric field co-modulation of the Schottky barrier profile at the ITO/Nb:SrTiO₃ interface, the oxide heterojunction device can respond to the entire visible light region in a neuromorphic manner, allowing synaptic paired-pulse facilitation, short/long-term memory, and "learning-experience" behavior for optical information manipulation. More importantly, the photoplasticity of the artificial synapse has been modulated by heterosynaptic means with a sub-1 V external voltage, not only enabling an optoelectronic analog of the mechanical aperture device showing adaptive and stable optical perception capability under different illuminating conditions but also making the artificial synapse suitable for the mimicry of interest-modulated human visual memories.

All-Oxide-Based Highly Transparent Photonic Synapse for Neuromorphic Computing

The neuromorphic system processes enormous information even with very low energy consumption, which practically can be achieved with photonic artificial synapse. Herein, a photonic artificial synapse is demonstrated based on an all-oxide highly transparent device. The device consists of conformally grown In₂O₃/ZnO thin films on a fluorine-doped tin oxide/glass substrate. The device showed a loop opening in current-voltage characteristics, which was attributed to charge trapping/detrapping. Ultraviolet illumination-induced versatile features such as short-term/long-term plasticity and paired-pulse facilitation were truly confirmed. Further, photonic potentiation and electrical habituation were implemented. This study paves the way to develop a device in which current can be modulated under the action of optical stimuli, serving as a fundamental step toward the realization of low-cost synaptic behavior.