Synaptic Devices Research Articles

A bioinspired neuromuscular system enabled by flexible electro-optical N2200 nanowire synaptic transistor

Mimicking the functional traits of the muscle system evolves the development of the neuromorphic prosthetic limbs. Herein, a bioinspired neuromuscular system was constructed by connecting an information processor that uses a flexible electro-optical synaptic transistor (FNST) to an effector that uses artificial muscle fibers. In this system, the response of artificial muscle fibers, which imitate the movement of biological muscle fibers, is manipulated by neuromorphic synaptic devices. The FNST is regulated by light pulses and electrical spikes to emulate biological synaptic functions, and thereby applied in secure communication. The feasibility of n-type organic nanowires acting as the channels for neuromorphic devices was demonstrated. Attributing to the flexibility of the n-type organic semiconductor N2200 nanowires, the current of the FNST retains >85% of its initial value after the 5000 bending cycles to radius = 1 cm. The tolerance of bending of the FNST implies its potential applications in wearable electronics. This work offers an approach to potentially advancing electronic skin, neuro-controlled robots, and neuromorphic prosthetic limbs.

Nanocrystal Materials for Resistive Memory and Artificial Synapses: Progress and Prospects.

Resistive random-access memory (RRAM) is considered to be the most promising next-generation non-volatile memory because of its low cost, low energy consumption, and excellent data storage characteristics. However, the on/off (SET/RESET) voltages of RRAM are too random to replace the traditional memory. Nanocrystals (NCs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-processed technologies. Therefore, the doping NCs in the function layer of RRAM are proposed to localize the electric field and guide conductance filaments (CFs) growth. The purpose of this article is to focus on a comprehensive and systematical survey of the NC materials, which are used to improve the performance of resistive memory (RM) and optoelectronic synaptic devices and review recent experimental advances in NC-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Extensive information related to NCs for RRAM and artificial synapses and their associated patents were collected. This review aimed to highlight the unique electrical and optical features of metal and semiconductor NCs for designing future RRAM and artificial synapses. It was demonstrated that doping NCs in the function layer of RRAM could not only improve the homogeneity of SET/RESET voltage but also reduce the threshold voltage. At the same time, it could still increase the retention time and provide the probability of mimicking the bio-synapse. NC doping can significantly enhance the overall performance of RM devices, but there are still many problems to be solved. This review highlights the relevance of NCs for RM and artificial synapses and also provides a perspective on the opportunities, challenges, and potential future directions.

Bismuth-based ferroelectric memristive device induced by interface barrier for neuromorphic computing

The development of the Internet of Things (IoT) not only facilitates our lives but also dramatically grows data. Artificial synaptic devices appear to represent an emerging physical solution compared to the existing computational architectures. Memristive devices are of great interest with their high integration and low power consumption in the field of synaptic devices. In this work, we demonstrate short-term plasticity (STP) and long-term plasticity (LTP) by regulating synaptic weights with different stimulus pulses. Moreover, the application in neuromorphic computing is further exhibited by image recognition with an accuracy of 95.2 % under the modified National Institute of Standards and Technology database. Therefore, Nd-doped Bi4Ti3O12 memristors, as emerging artificial synaptic devices, are expected to achieve breakthroughs in artificial intelligence and promote the development of neuromorphic computing and intelligent systems.

Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware

We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures.

Impact of deposition temperature on persistent photoconductivity of SnO2 thin films deposited using spray pyrolysis technique suitable in optoelectronic synaptic devices

In this study, nanocrystalline tin oxide thin films that exhibited enhanced persistent photoconductivity (PPC) were prepared using spray pyrolysis technique. The effect of deposition temperature on different aspects of photoconductivity of these films were analysed using different characterization techniques. Raman analysis confirmed the tetragonal rutile structure of the films and also indicated their nanocrystalline nature, while XPS analysis verified the presence and oxidation states of tin and oxygen in the films. The films demonstrated UV sensing capability under above-bandgap illumination, generating electron-hole pairs and enhancing electrical conductivity. The deposition temperature of the film influenced their photocurrent characteristics, showcasing varied responses in terms of photosensitivity and photocurrent. Enhancement of photoresponse was observed with the increase in deposition temperatures of the samples. Notably, films deposited at 450 and 500 °C exhibited the more persistent photocurrent at the tested deposition temperatures. The surface defects, oxygen vacancies and grain boundaries played a significant role in sustaining PPC by ionizing vacancies and creating persistent carriers even after light exposure by trapping free carriers and impeding recombination. From the exponential fitting and extrapolation of the decay curves, the sample prepared at 450 °C was observed to be the most suitable film in application point of view for fabricating devices that make use of persistent photoconductivity. Overall, this work highlights the effectiveness of modulation of deposition temperature in influencing photoconductor parameters of spray pyrolyzed tin oxide films, offering potential applications in non-volatile memory, bionic optoelectronics, neuromorphic computing and bioelectronics.

A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices.

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.

Electrical Control Grain Dimensionality with Multilevel Magnetic Anisotropy.

In alignment with the increasing demand for larger storage capacity and longer data retention, the electrical control of magnetic anisotropy has been a research focus in the realm of spintronics. Typically, magnetic anisotropy is determined by grain dimensionality, which is set during the fabrication of magnetic thin films. Despite the intrinsic correlation between magnetic anisotropy and grain dimensionality, there is a lack of experimental evidence for electrically controlling grain dimensionality, thereby impairing the efficiency of magnetic anisotropy modulation. Here, we demonstrate an electric field control of grain dimensionality and prove it as the active mechanism for tuning interfacial magnetism. The reduction in grain dimensionality is associated with a transition from ferromagnetic to superparamagnetic behavior. We achieve a nonvolatile and reversible modulation of the coercivity in both the ferromagnetic and superparamagnetic regimes. Subsequent electrical and elemental analysis confirms the variation in grain dimensionality upon the application of gate voltages, revealing a transition from a multidomain to a single-domain state, accompanied by a reduction in grain dimensionality. Furthermore, we exploit the influence of grain dimensionality on domain wall motion, extending its applicability to multilevel magnetic memory and synaptic devices. Our results provide a strategy for tuning interfacial magnetism through grain size engineering for advancements in high-performance spintronics.

Synaptic device-based neuromorphic computing in artificial intelligence

The application of synaptic device-based neuromorphic computing in artificial intelligence is an emerging research field aimed at simulating the structure and function of the human brain and realizing high-efficiency, low-power, and adaptive intelligent computing. This paper reviews the principles, growth and challenges of neuromorphic devices based on synapses computing and its applications and perspectives in artificial intelligence fields like an image processing as well as natural language processing. The paper first introduces the basic concepts, properties and classification of synaptic devices, as well as the basic framework and algorithms of neuromorphic computing. Then, the paper analyzes the advantages and difficulties of neuromorphic computing based on synaptic devices, including the preparation, testing, modelling and integration of the devices, as well as the systems architecture, programming and optimization. Then, this paper gives examples of the applications and effects of synaptic device-based neuromorphic computing in artificial intelligence fields such as image processing and natural language processing, including image denoising, image segmentation, image recognition, text classification, text summarization, and text generation. Finally, this paper summarizes the current research status and future synaptic device-based neuromorphic computing trends. It puts forward some research directions and suggestions to promote the development and innovation in this field.

Flexible Low‐Voltage MXene Floating‐Gate Synaptic Transistor for Neuromorphic Computing and Cognitive Learning

AbstractNeuromorphic computing, inspired by the functionality of biological neural networks, has emerged as a promising paradigm for artificial intelligence applications, especially in the field of flexible electronics. Among the various artificial synaptic devices, floating‐gate synaptic transistors exhibit long‐term synaptic plasticity, but they face the challenge of achieving flexible compatibility. In this work, the first demonstration of a flexible MXene floating‐gate synaptic transistor is reported, which uses multiple layers of MXene as floating gates and MXene nanosheets as charge state modulators. The device shows excellent mechanical flexibility and can operate at low voltages, which improves its suitability for wearable electronic devices. It can also emulate Pavlovian conditioned reflexes under external stress, suggesting its potential for cognitive learning. Moreover, the device is utilized for handwritten digit recognition by simulating a fully connected neural network, achieving a high recognition accuracy of 92.0%. This demonstrates its practical applicability in neuromorphic computing. Besides, this research achieves the patterning of MXene and its application in flexible floating‐gate transistors. It provides a new solution for the integrated fabrication of flexible artificial synaptic devices.

Introducing Chiro-optical Activities in Photonic Synapses for Neuromorphic Computing and In-Memory Logic Operations.

In artificial synaptic devices aimed at mimicking neuromorphic computing systems, electrical or optical pulses, or both, are generally used as stimuli. In this work, we introduce chiral materials for tailoring the characteristics of photonic synaptic devices to achieve handedness-dependent neuromorphic computing and in-memory logic gates. In devices based on a pair of chiral perovskites, the use of circularly polarized light (CPL) as the optical stimuli mimicked a series of electrical and opto-synaptic functionalities in order to emulate the multifunctional complex behavior of the human brain. Upon illumination in this two-terminal device, anisotropy in current has been observed due to the out-of-plane carrier transport, originating from spin-selective carrier transport. More importantly, the logic gate achieved in devices based on optoelectronic memristors turned out to be chirality-dependent; while an R-device functioned as an AND gate, the device based on the same perovskite of the opposite chirality (S-device) acted as a NOR gate toward in-memory logic operations. These findings in chiral perovskite-based artificial synapses can identify further strategies for future neuromorphic computing, vision simulation, and artificial intelligence.

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems.

In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

Advanced Neuromorphic Applications Enabled by Synaptic Ion-Gating Vertical Transistors.

Bioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi-modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion-gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain-like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting-edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low-voltage multi-sensing IGVTs for artificial-synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi-stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi-modal flexible sensor technologies and presents a roadmap for future high-end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.

A sensory–neuromorphic interface capable of environmental perception, sensory coding, and biological stimuli

AbstractThe sensory–neuromorphic interface is key to the application of neuromorphic electronics. Artificial spiking neurons and artificial sensory nerves have been created, and a few studies showed a complete neuromorphic system through cointegration with synaptic electronics. However, artificial synaptic devices and systems often do not work in real environments, which limits their ability to provide realistic neural simulations and interface with biological nerves. We report a sensory–neuromorphic interface that uses a fiber synapse to emulate a biological afferent nerve. For the first time, a sensing–neuromorphic interface is connected to a living organism for peripheral nerve stimulation, allowing the organism to establish a connection with its surrounding environment. The interface converts perceived environmental information into analog electrical signals and then into frequency‐dependent pulse signals, which simplify the information interface between the sensor and the pulse‐data processing center. The frequency of the interface shows a sublinear dependence on strain amplitude at different stimulus intensities, and can deliver increased frequency spikes at potentially damaging stimulus intensities, similar to the response of biological afferent nerves. To verify the application of this interface, a system that monitors strain and provides an overstrain alarm was constructed based on this afferent neural circuit. The system has a response time of <2 ms, which is compatible with the response time in biological systems. The interface can be potentially extended to process signals from almost any type of sensors for other afferent senses, and these results demonstrate the potential for neuromorphic interfaces to be applied to bionic sensory interfaces.

Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space.

Arthropods' eyes are effective biological vision systems for object tracking and wide field of view because of their structural uniqueness; however, unlike mammalian eyes, they can hardly acquire the depth information of a static object because of their monocular cues. Therefore, most arthropods rely on motion parallax to track the object in three-dimensional (3D) space. Uniquely, the praying mantis (Mantodea) uses both compound structured eyes and a form of stereopsis and is capable of achieving object recognition in 3D space. Here, by mimicking the vision system of the praying mantis using stereoscopically coupled artificial compound eyes, we demonstrated spatiotemporal object sensing and tracking in 3D space with a wide field of view. Furthermore, to achieve a fast response with minimal latency, data storage/transportation, and power consumption, we processed the visual information at the edge of the system using a synaptic device and a federated split learning algorithm. The designed and fabricated stereoscopic artificial compound eye provides energy-efficient and accurate spatiotemporal object sensing and optical flow tracking. It exhibits a root mean square error of 0.3 centimeter, consuming only approximately 4 millijoules for sensing and tracking. These results are more than 400 times lower than conventional complementary metal-oxide semiconductor-based imaging systems. Our biomimetic imager shows the potential of integrating nature's unique design using hardware and software codesigned technology toward capabilities of edge computing and sensing.

MoS2/ZnO-heterostructured optoelectronic synapse for multiwavelength optical information-based sensing, memory, and processing

The rapid development of artificial intelligence and humanoid robots requires hardware-level revolution of synapses-based bionic vision system. However, current optoelectronic synapses featuring multiwavelength sensing and multifunctional processing necessitate intricate integrations of multi-materials and floating gate configurations, which induces complex fabrication process and limited integration density. Here, we propose a novel two-terminal optoelectronic synaptic device based on MoS2/ZnO heterostructure which integrates sense and memory functions and realizes multifunctional optical information processing while circumvents the need of complex configurations. By simultaneously exploiting the photo-adsorption oxygen dissociation property of ZnO and the persistent photoconductivity effect of MoS2, the device demonstrates synaptic capability of four-color perception with a single synaptic event consuming a mere 2.55×10−9 J. Different from the traditional power-density sensitive nociceptor, for the first time photon energy-sensitive nociceptors is developed, which can mimic the process of UV irradiation to the retina leading to fear of visible light. Diverse information processing ability, such as photonic information encryption, associative learning, and logic calculation, was demonstrated in a single device, indicating a practical strategy for facile design for future high integration intensity neuromorphic devices.

Highly uniform organic nanowire synaptic arrays with excellent performance for associative memory

Synaptic devices of optoelectronic nature, which combine light-detection and data-storage capabilities, hold significant promise in the realm of neuromorphic computing. They are especially beneficial for the processing of visual data and the execution of intricate cognitive functions akin to learning, memory retention, and logical reasoning. However, current research is mostly confined to the level of individual devices, with corresponding studies on synaptic arrays being relatively scarce. Type II heterojunctions are widely used in optoelectronics. Bandgap engineering enables efficient separation and trapping of photogenerated excitons in selected materials, reducing carrier recombination. This prolongs carrier retention in the channel, delaying photocurrent decay, crucial for artificial optoelectronic synapses. We designed an organic nanowire/perovskite Type II heterojunction where CsPbBr3 perovskite films and TIPS nanowires serve as the photosensitive and channel layers, respectively. The composite synaptic array was successfully fabricated by in-situ growth of TIPS nanowires on the surface of perovskite thin films. It was further used to fabricate a photonic synapse array that exhibits characteristic photocurrent and stable optical response. Achieving high-performance photonic synapses was facilitated by characteristics such as high mobility and high on/off ratios, stemming from the formation of complete single-crystal nanowires on the perovskite films. Exhibiting synaptic behaviors such as photo-induced enhancements and paired-pulse facilitation, the device enabled a visual sensing system with a 5 × 5 pixel array for simulating memory processes and restoring associative memories. A novel photocurrent model was established for understanding device characteristics. This work provides valuable insights for future utilization of composite organic nanowire arrays in simulating brain-like computations and realizing large-scale intelligent visual sensing systems.

Overshoot‐Suppressed Memristor Array with AlN Oxygen Barrier for Low‐Power Operation in the Intelligent Neuromorphic Systems

As the demand for bio‐inspired neuromorphic systems grows, memristor has emerged as a pivotal component in artificial synaptic devices. This study delves into the advantages and limitations of the one‐transistor‐one‐resistor (1T‐1R) and 0T‐1R architectures for memory array configurations. A significant enhancement in the memristor's on/off ratio, surpassing 103, achieved by integrating both an overshoot suppression layer (OSL) and an ultra‐thin AlN oxygen barrier layer (OBL) is also reported. The concurrent insertion of an AlOx OSL is essential for manifesting the self‐compliance attribute in 4F2 memristor arrays. Evaluations of a 24 × 24 array embedded with OSL and OBL reveal a substantial reduction in retention variation. In terms of functionality, a 7.13‐fold decline in vector‐matrix multiplication error is observed, accentuating the potential of this approach for neural network synapse applications. The analog reset characteristics of the OBL memristor facilitate over 25 multilevel states. Furthermore, the adoption of nonnegative weights presents an avenue to potentially double synaptic integration density. Through simulation program with integrated circuit emphasis simulations, the necessity of nonnegative and 16‐level quantized conductance in balancing power consumption without accuracy loss at the image classification is validated.

Role of Ti interfacial layer in the stability of TiO2 based transparent synaptic device

Interface engineering plays a pivotal role in manipulating the electrical transport and conduction mechanism of a synaptic device. In this work, the impact of Ti as an interfacial layer is systematically investigated by inserting ∼5 nm thin film at both interfaces of a functional layer (TiO2). Interestingly, it was observed that Ti layers significantly regulate the migration of oxygen ions/vacancies at the interfaces yielding an improved stability from 10 to 200 cycles, sustained over a longer period ∼8 × 103 s. Forming free and gradual transition in conductance on positive bias region under a controlled compliance current ∼0.3–17 mA demonstrates the multilevel switching highlighting the typical synaptic behavior of memristor. The ohmic conduction and space charge-limited current mechanism was found across the various resistive states signifies the trapping/de-trapping. Besides, other key parameters of synaptic device such as paired pulse facilitation, depression, and short-term memory together with the excellent transmittance in the visible spectral range makes our device adequate for innovative transparent neuromorphic applications.

Capacitorless Two‐Transistor Dynamic Random‐Access Memory Cells Comprising Amorphous Indium–Tin–Gallium–Zinc Oxide Thin‐Film Transistors for the Multiply–Accumulate Operation

AbstractCapacitorless two‐transistor (2T0C) dynamic random‐access memory (DRAM) cells comprising oxide thin‐film transistors (TFTs) show potential as low‐power and high‐density DRAM cells; however, the multiply–accumulate (MAC) operation using these cells is not yet realized. In this study, 2T0C DRAM cells comprising amorphous indium–tin–gallium–zinc oxide TFTs are fabricated for MAC operations. In a 2T0C DRAM cell, one transistor acts as a write transistor and the other as a read transistor, whose gate capacitance corresponds to the data storage capacitance. The cells have a long retention time of 1000 s, which is 104 times longer than that of conventional DRAM cells, owing to the extremely low leakage current of the TFTs (1.11 × 10−18 A µm−1). These cells satisfy the original condition for synaptic devices, in which a proportional relationship exists between the input and output. The MAC operation is performed using two cells. This study demonstrates the usefulness of oxide TFTs in artificial neural networks.

N-Type ion gel gated vertical organic electrochemical transistors based on benzodifurandione-based Oligo(p-phenylene vinylene)s

Organic electrochemical transistors (OECTs) using aqueous gate dielectrics have garnered significant interest for bioelectronic applications. However, their viability for long-term use in neuromorphic computing and synaptic devices is limited due to their short-term functionality. In this study, we synthesize two benzodifurandione-based oligo (p-phenylene vinylene) polymers, BDOPV-TCNVT and ClBDOPV-TCNVT, and investigate their electrochemical transistor properties using quasi-solid-state ion gel-gated vertical OECTs (v-OECTs). Compared to BDOPV-TCNVT, the chlorinated ClBDOPV-TCNVT demonstrates lower frontier molecular orbitals and easier electrochemical doping. The higher volumetric capacitance of as-spun ClBDOPV-TCNVT (1.94 F cm−3) compared to as-spun BDOPV-TCNVT films (1.49 F cm−3) is mainly attributed to the easier ion infiltration resulting from its lower crystallinity with mixed chain orientation. The quasi-solid-state v-OECTs based on both polymers (as-spun) exhibit transconductance (gm) of 0.06–0.08 mS. Following thermal treatments, the gm gradually decreases for both polymers due to enhanced edge-on ordering with tight interchain packing, hindering ion penetration. Despite the poor electrochemical doping by quasi-solid-state ion gel gated dielectrics, the enlarged area and decreased channel length in v-OECTs (compared to parallel OECTs) can enhance the gm. Further optimization of v-OECTs requires tailored material designs specifically suited for efficient vertical charge transport together with ion infiltration.