Synaptic Transistors Research Articles

Ultrasensitive Flexible Organic Synaptic Transistors Modulated by a Chemically Cross-Linked Solvent-Resistive Ion Composite.

We demonstrate a flexible organic synaptic transistor (FOST) with an ion-composite electrolyte film resistant to chemical reagents, which uses a three-dimensionally cross-linked polyimide matrix to accommodate a high-concentration ionic liquid. FOST shows versatile synaptic plasticity for classical conditioning, high-pass filtering, and the learning-forgetting process. The device achieves low-energy consumption down to 1.02 femtojoule per synaptic event with an ultrasensitive impulse to presynaptic spike down to 0.5 mV. Moreover, the electrical performance of the device is still stable after 1000 mechanical bending cycles. These results demonstrate that the device can be applied to future flexible neuromorphic electronics.

Scalable Synaptic Transistor Memory from Solution-Processed Carbon Nanotubes for High-Speed Neuromorphic Data Processing.

Neural networks as a core information processing technology in machine learning and artificial intelligence demand substantial computational resources to deal with the extensive multiply-accumulate operations. Neuromorphic computing is an emergent solution to address this problem, allowing the computation performed in memory arrays in parallel with high efficiencies conforming to the neural networks. Here, scalable synaptic transistor memories are developed from solution-sorted carbon nanotubes. The transistors exhibit a large switching ratio of over 105, a significant memory window of ≈12V arising from charge trapping, and low response delays down to tens of nanoseconds. These device characteristics endow highly stabilized reconfigurable conductance states, successful emulation of synaptic functions, and a high data processing speed. Importantly, the devices exhibit uniform characteristic metrics, e.g., with a 1.8% variation in the memory window, suggesting an industrial-scale manufacturing capability of the fabrication. Using the memories, a hardware convolution kernel is designed and parallel image processing is demonstrated at a speed of 1M bit per second per input channel. Given the efficacy of the convolution kernel, a promising prospect of the memories in implementing neuromorphic computing is envisaged. To explore the potential, large-scale convolution kernels are simulated and high-speed video processing is realized for autonomous driving.

All‐Polymer Organic Electrochemical Synaptic Transistor With Controlled Ionic Dynamics for High‐Performance Wearable and Sustainable Reservoir Computing

All‐Polymer Organic Electrochemical Synaptic Transistor With Controlled Ionic Dynamics for High‐Performance Wearable and Sustainable Reservoir Computing

Two-Dimensional MoS2-Based Anisotropic Synaptic Transistor for Neuromorphic Computing by Localized Electron Beam Irradiation.

Neuromorphic computing, a promising solution to the von Neumann bottleneck, is paving the way for the development of next-generation computing and sensing systems. Axon-multisynapse systems enable the execution of sophisticated tasks, making them not only desirable but essential for future applications in this field. Anisotropic materials, which have different properties in different directions, are being used to create artificial synapses that can mimic the functions of biological axon-multisynapse systems. However, the restricted variety and unadjustable conductive ratio limit their applications. Here, it is shown that anisotropic artificial synapses can be achieved on isotropic materials with externally localized doping via electron beam irradiation (EBI) and purposefully induced trap sites. By employing the synapses along different directions, artificial neural networks (ANNs) are constructed to accomplish variable neuromorphic tasks with optimized performance. The localized doping method expands the axon-multisynapse device family, illustrating that this approach has tremendous potentials in next-generation computing and sensing systems.

Inhibiting the current spikes within the channel layer of LiCoO2-based three-terminal synaptic transistors

Synaptic transistors, which emulate the behavior of biological synapses, play a vital role in information processing and storage in neuromorphic systems. However, the occurrence of excessive current spikes during the updating of synaptic weight poses challenges to the stability, accuracy, and power consumption of synaptic transistors. In this work, we experimentally investigate the main factors for the generation of current spikes in the three-terminal synaptic transistors that use LiCoO2 (LCO), a mixed ionic-electronic conductor, as the channel layer. Kelvin probe force microscopy and impedance testing results reveal that ion migration and adsorption at the drain–source-channel interface cause the current spikes that compromise the device's performance. By controlling the crystal orientation of the LCO channel layer to impede the in-plane migration of lithium ions, we show that the LCO channel layer with the (104) preferred orientation can effectively suppress both the peak current and power consumption in the synaptic transistors. Our study provides a unique insight into controlling the crystallographic orientation for the design of high-speed, high-robustness, and low-power consumption nano-memristor devices.

Advanced Dual-Input Artificial Optical Synapse for Recognition and Generative Neural Network

Perovskite materials have emerged as leading candidates for optical synaptic devices due to their superior photosensitivity and tunable optoelectronic properties. However, the practical application of perovskite-based optoelectronic synaptic transistors has been hindered by issues of poor stability and high toxicity. This study developed an artificial synaptic thin film transistor (TFT) based on a Cs2AgBiBr6/InOx bilayer. The device demonstrated synaptic behavior under optoelectronic hybrid stimulation (largest wavelength ~520nm), such as excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), short-term memory (STM) and long-term memory (LTM). Notably, the “light writing and voltage erasing” characteristics of these devices could be utilized to construct a convolutional neural network (CNN) classifier for the CIFAR-10 dataset, demonstrating noise tolerance close to the human eye. The loss in recognition accuracy was within 1% when Gaussian white noise and salt pepper noise were added. Furthermore, these devices exhibited great potential in Generative Adversarial Networks (CycleGAN), with the generated image quality achieving levels of 0.637 and 0.715 in Improved Perceptual Image Processing System (IPIPS) and Structural Similarity Index (SSIM) evaluation metrics for the zebra dataset, indicating good image quality. This study indicates that our Cs2AgBiBr6-based artificial optical synaptic thin-film transistors (TFTs) are promising for sensing and in-memory computing applications.

Reconfigurable Five-In-One Carbon Nanotube Optoelectronic Transistor for Intelligent Computing and Communication.

Advances in artificial general intelligence (AGI) necessitate the integration of diverse functionalities to address complex tasks. Carbon nanotubes (CNTs), with their unique physical properties, have broad applications in emerging research fields, providing a foundation for next-generation devices that could overcome the limits of Moore's Law. This work demonstrates a novel intelligent device that integrates five functions─sensors, memory, neuromorphic computing, logic, and communication─using CNT field-effect transistors (CNFETs) compatible with CMOS processes. Through passivation and annealing techniques, we have significantly enhanced the optoelectronic performance of CNFETs, leading to the development of multifunctional optoelectronic synaptic transistors. These optimized CNFETs enable dual-mode weight-tunable synaptic functions, including long-term plasticity and multilevel storage. Additionally, a CNT-based neural network has achieved high recognition accuracy on the MNIST data set, showcasing the potential of in-memory computing. This research also innovates by integrating logic functions with optoelectronic communication capabilities, paving the way for next-generation intelligent computing and communication integrated systems.

Atomic Nb-doping of WS2 for high-performance synaptic transistors in neuromorphic computing

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS2 and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 103, 100 times larger than that of devices prepared with undoped WS2. The Nb atoms in WS2 play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS2 synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study’s contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.

Toward Enhanced Biomimetic Artificial Visual Systems Based on Organic Heterojunction Optoelectronic Synaptic Transistors

AbstractArtificial visual systems, inspired by the human eye, hold significant potential in artificial intelligence. Optoelectronic synapses, integrating image perception, processing, and memory in a single device, offer promising solutions. The human eye exhibits different recognition accuracies for objects under varying light conditions. Therefore, a more biomimetic visual system is needed to better fit actual application scenarios. Here, an organic heterojunction‐based optoelectronic synaptic transistor (OHOST) is proposed to enhance biomimetic artificial visual systems. By utilizing the excellent carrier capture ability of core‐multi‐shell quantum dots (QDs) and the high exciton dissociation efficiency of heterojunction interfaces, the device achieves a recognition capability under different light intensities closely resembling that of the human eye. Under optimal light intensity, the recognition accuracy for the modified national institute of standards and technology (MNIST) dataset can reach 91.52%. Nevertheless, under both low and high light intensities, the accuracy drops to a low level. This work pushes the development of artificial visual systems toward higher levels of biomimicry.

An Ultrasensitive Programmable 2D Photoelectric Synaptic Transistor

AbstractThe burgeoning advancement of information technology has engendered a discernible surge in the examination of neuromorphic devices, notably drawing broader attention to artificial vision systems endowed with sensory recognition capabilities. Current photoelectric synapse devices employed in artificial vision systems are generally well‐suited for well‐illuminated conditions, yet exhibit diminished sensitivity in weak‐light scenarios, resulting in a pronounced deterioration of recognition accuracy. Here, an ultrasensitive photoelectric synaptic transistor based on negative quantum capacitance effect resulted from the 2D semi‐metallic graphene layer that partially enclosed within the gate dielectric layer, which manifests a noteworthy reduction in device control voltage and exhibits perception and storage capabilities for weak light of 39.4 nW cm−2 with detectivity above 1016 cm Hz1/2 W−1 is demonstrated. The voltage amplification effect and the concomitant formation of an equivalent local electrostatic field induced by the negative quantum capacitance effect engenders a robust programmable synaptic plasticity for extremely weak light by modifying the control gate. These results represent the inaugural integration of the negative quantum capacitance effect into optoelectronic devices and furnish a robust hardware foundation for developing vision systems in weak‐light environments.

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

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

Low-Cost, High-Efficiency Aluminum Zinc Oxide Synaptic Transistors: Blue LED Stimulation for Enhanced Neuromorphic Computing Applications.

Neuromorphic devices are electronic devices that mimic the information processing methods of neurons and synapses, enabling them to perform multiple tasks simultaneously with low power consumption and exhibit learning ability. However, their large-scale production and efficient operation remain a challenge. Herein, we fabricated an aluminum-doped zinc oxide (AZO) synaptic transistor via solution-based spin-coating. The transistor is characterized by low production costs and high performance. It demonstrates high responsiveness under UV laser illumination. In addition, it exhibits effective synaptic behaviors under blue LED illumination, indicating high-efficiency operation. The paired-pulse facilitation (PPF) index measured from optical stimulus modulation was 179.6%, indicating strong synaptic connectivity and effective neural communication and processing. Furthermore, by modulating the blue LED light pulse frequency, an excitatory postsynaptic current gain of 4.3 was achieved, demonstrating efficient neuromorphic functionality. This study shows that AZO synaptic transistors are promising candidates for artificial synaptic devices.

Artificial pain-perceptual nociceptor emulation based on graphene oxide synaptic transistors

Pain-perceptual nociceptors play a crucial role as sensory neurons, detecting noxious stimuli and enabling the human body to respond effectively while accurately perceiving atypical or hazardous situations in reality. Hence, the utilization of emerging devices in hardware to incorporate pain-perceptual nociceptors can significantly enhance the effectiveness of bionic devices by providing them with varying sensitivities towards external stimuli based on specific objectives. In this study, we have developed a pain-perceptive nociceptor by combining a temperature sensor with a synaptic transistor. To emulate synaptic functions and features of nociceptors, a graphene oxide synaptic transistor with sodium alginate electrolyte as gate dielectric is utilized. This device has the ability to replicate significant attributes of pain-perceptive nociceptors, including the threshold for pain, memory of prior injury, and modulation of pain sensitivity. For the purpose of perceiving pain, temperature sensing is achieved by employing a MoS2 temperature sensor for the pain-perceptual nociceptors. The suggested device has the potential to create novel possibilities for the applications of advanced neuromorphic brain-like systems, like electronic skins, wearable sensing and implantable bioelectronics.

Emulating Low-Power Synaptic Plasticity in a Solution-Processed Oxide-Based Long Retention Memory Transistor with High Learning Accuracy.

The exploration of synaptic plasticity in metal-oxide-based ferroelectric thin-film transistors has been limited. As a perovskite ferroelectric material, LiNbO3 is widely studied; but its potential use as a neuromorphic device, like synaptic transistors, has not been realized. In this study, a solution-processed ferroelectric thin-film transistor (FeTFT) with an alternating layer of LiNbO3 and Li5AlO4 as a gate dielectric has been fabricated. This configuration reduces the depolarization field by leveraging the large ionic polarization of Li+ ions in the Li5AlO4 layer, while the wide bandgap helps mitigate the leakage current. FeTFT exhibits impressive transistor performance, including a saturation mobility of 0.478 cm2V-1 s-1, an on/off ratio of 3.08 × 103, and a low trap-state density of 1.3 × 1013 cm-2. Moreover, the device demonstrates good memory retention, retaining information for nearly 1 day. It successfully emulates synaptic plasticity, specifically short-term plasticity and long-term plasticity. Besides, a 94% training accuracy has been achieved through artificial neural network simulation. Notably, the FeTFT consumes minimal power, with energy consumption of approximately 3.09 nJ per synaptic event, which is remarkably low compared to other reported solution-processed FeTFT devices.

Linearly Tunable Optoelectronic Synaptic Transistor for In-Sensor Reservoir Computing

Linearly Tunable Optoelectronic Synaptic Transistor for In-Sensor Reservoir Computing

Vacancy-driven resistive switching behavior based on wafer-scale MoSe2 artificial synapses

Recently, researches have revealed that synaptic transistors based on two-dimensional materials (2DMs) have substantial advantages and potential for modeling neural synapses. However, the mechanism of artificial synaptic device with 2DMs as conducting channels remains to be further clarified. Here, high-quality wafer-scale MoSe2 nanosheets were achieved by ALD-CVD process, where further 23 × 23 × 4 synaptic arrays were fabricated in situ. Notably, the outstanding MoSe2 synaptic device exhibits an on/off ratio of 3.6 × 107 with hysteresis ratio of 6.8k and simulates crucial biological synaptic behaviors such as paired pulse facilitation and excitatory postsynaptic currents. The mechanism of Se vacancy induction and horizontal diffusion driven is revealed based on high-resolution transmission electron microscopy characterization with first-principles calculations, which suggests a possible mechanistic explanation for the resistive switching behavior of 2DMs transistors. Artificial synaptic devices with 2DMs based on the working mechanism of vacancy diffusion are capable of satisfying the needs of complex neural network applications.

Retina-Inspired Large-Area Solution-Processed flexible Multi-Band organic neuromorphic synaptic transistor arrays

The proliferation of high-efficacy, pliable organic neuromorphic synaptic transistors is quintessential for the integration into wearable artificial intelligence ecosystems. Nevertheless, their development is hindered by substantial energy demands and suboptimal charge carrier mobility. To surmount these obstacles, we exploit solution-processed poly(amic acid) (PAA) dielectrics and organic semiconductors to engineer pliable arrays of organic neuromorphic synaptic transistors atop a polyethylene terephthalate (PET) matrix. The polar group and nanogroove structure of PAA obtain excellent semiconductor crystal structure, and the excellent dielectric properties enable these engineered arrays to show a charge carrier mobility peak of 29.83 cm2 V−1 s−1 an aggregate mean mobility of 15.87 cm2 V−1 s−1 (standard deviation is 6.71) at a voltage of −3 V and the energy expenditure per synaptic transaction was only 0.26 fJ. Additionally, the transistors possess the aptitude for photodetection across ultraviolet–visible to near-infrared spectral bands. The successful execution of flexible imaging, Pavlovian classical conditioning reflex associative learning, coupled with a 93.9 % accuracy in handwritten numeral recognition, corroborates the viability of these high-mobility, low-power organic neuromorphic synaptic transistor arrays.

Comprehensively Modulated Sub-Attojoule Operated Optoelectronic Synapses for Image Encryption and Inpainting.

High-performance optoelectronic synaptic transistors play a crucial role in developing and emulating artificial visual systems. However, due to the predominant use of single-structure material modulation in optimizing optoelectronic synapses, their energy consumption significantly trails behind that of electronic synapses by several orders of magnitude. Herein, polymer dielectric layers and optimized contact strategies are adopted to realize the ultralow consumption optoelectronic synapses. Integration of polyimide dielectric significantly enhances photogenerated charge carrier dissociation, leading to substantial improvements in photoresponsivity (1.5 × 106 A·W-1), photodetectivity (6.9 × 1012 Jones), and external quantum efficiency (4.0 × 108%). Additionally, optimized contact properties augment their appeal for ultralow energy consumption in optoelectronic synapse applications. Excitatory postsynaptic current is triggered at an incredibly low voltage of 5 μV and boosts an impressively low energy consumption of 0.05 aJ, ranking among the best-reported results in this field. Next, we demonstrate an integrated system combining the MoS2 optoelectronic synapses with a recurrent neural network enabling 100% accurate recognition of optical signals, particularly in scenarios with aJ-leveled energy consumption. Finally, an image encryption system has been developed, in which images are encrypted by photoelectronic conversion of synapse arrays with random voltage settings and decrypted according to the recurrent neural network-based accuracy. More importantly, once partially damaged images are encrypted, through the decryption image inpainting can be realized due to the high accuracy. The proposed innovative approach holds promise for advancing artificial intelligence applications with improved energy efficiency, information security, and computational capabilities.

Artificial synapse based on low-voltage Ni-doped CuI thin-film transistors for neuromorphic application

Inspired by the human brain's capacity as a powerful biological computer capable of simultaneously processing a vast array of cognitive tasks, many emerging artificial synapse devices have been developed in recent years. Electric-double-layer (EDL) transistors based on interfacial ion-modulation have attracted widespread attention for simulating synaptic plasticity and neural functions. Here, low-voltage EDL p-type thin-film transistors (TFTs) are fabricated on glass substrates, with Ni-doped cuprous iodide (Ni0.06Cu0.94I) as the channel and chitosan as the dielectric. The electrical performance of the Ni0.06Cu0.94I TFTs is investigated: current on/off ratio of 6.4 × 104, subthreshold swing of 33 mV/dec, threshold voltage of 1.38 V, operating voltage of 2 V, and saturation field-effect mobility of 15.75 cm2 V−1 s−1. A dual in-plane gate OR logic operation is demonstrated. Importantly, by applying single voltage pulses, dual voltage pulses, and multiple voltage pulses to the gate, the Ni0.06Cu0.94I transistors exhibited typical synaptic characteristics, including short-term potentiation, short-term depression, long-term potentiation, long-term depression, paired-pulse facilitation, and spiking-rate-dependent plasticity. Furthermore, the synaptic transistor can also simulate the learning–forgetting–relearning process of the human brain. These remarkable behaviors of voltage-stimulated synaptic transistors have potential for neuromorphic applications in future artificial systems.

Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction.

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF3: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF3: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.