Dual-Mode Semiconductor Device Enabling Optoelectronic Detection and Neuromorphic Processing with Extended Spectral Responsivity.

High-performance semiconductor devices capable of multiple functions are pivotal in meeting the challenges of miniaturization and integration in advanced technologies. Despite the inherent difficulties of incorporating dual functionality within a single device, a high-performance, dual-mode device is reported. This device integrates an ultra-thin Al2O3 passivation layer with a PbS/Si hybrid heterojunction, which can simultaneously enable optoelectronic detection and neuromorphic operation. In mode 1, the device efficiently separates photo-generated electron-hole pairs, exhibiting an ultra-wide spectral response from ultraviolet (265 nm) to near-infrared (1650 nm) wavelengths. It also reproduces high-quality images of 256 × 256 pixels, achieving a Q-value as low as 0.00437 µW cm- 2 at a light intensity of 8.58 µW cm- 2. Meanwhile, when in mode 2, the as-assembled device with typical persistent photoconductivity (PPC) behavior can act as a neuromorphic device, which can achieve 96.5% accuracy in classifying standard digits underscoring its efficacy in temporal information processing. It is believed that the present dual-function devices potentially advance the multifunctionality and miniaturization of chips for intelligence applications.

PEDOT:PSS Wire: A Two-Terminal Synaptic Device for Operation in Electrolyte and Saline Solutions.

Synaptic devices, which are designed to emulate the synaptic functions of neurons, have recently gained attention as key elements in the development of neuromorphic hardware. To date, most synaptic devices utilizing conductive polymer materials, particularly poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), have been designed as three-terminal devices. Nevertheless, a recent study revealed that a single PEDOT:PSS wire can function as a two-terminal synaptic device through additional polymerization, which creates asymmetry in the wire diameter between the anode and cathode. Owing to its high biocompatibility, PEDOT is considered a promising candidate for use in clinical information-processing devices. However, previous studies examined the synaptic function of PEDOT:PSS only in PSS solutions. Therefore, the performance of PEDOT:PSS wires in other solutions, such as physiological saline solutions, remains unknown. Herein, we investigated the effects of operating environmental conditions (including phosphate-buffered saline (PBS)) on the synaptic functions of the asymmetric PEDOT:PSS wire. Our results indicate that the synaptic conductance change in the PEDOT:PSS wire occurred in all investigated aqueous electrolyte solutions. Moreover, we revealed the relationship between the synaptic conductance change behavior and the molecular properties of the electrolyte ions. Furthermore, the waveform of the conductance change can be controlled by adjusting the conditions for wire asymmetrization. These results demonstrate that the PEDOT:PSS wire exhibits a synaptic conductance change, yielding a waveform suitable for machine learning, even under wet conditions (i.e., in any electrolyte solution, including PBS). Therefore, PEDOT:PSS wire is a promising material for two-terminal synaptic devices applicable in clinical studies.

A neuromorphic device mimicking synaptic plasticity under different body fluid K+ homeostasis for artificial reflex path construction and pattern recognition.

The ionic environment of body fluids influences nervous functions for maintaining homeostasis in organisms and ensures normal perceptual abilities and reflex activities. Neural reflex activities, such as limb movements, are closely associated with potassium ions (K+). In this study, we developed artificial synaptic devices based on ion concentration-adjustable gels for emulating various synaptic plasticities under different K+ concentrations in body fluids. In addition to performing essential synaptic functions, potential applications in information processing and associative learning using short- and long-term plasticity realized using ion concentration-adjustable gels are presented. Artificial synaptic devices can be used for constructing an artificial neural pathway that controls artificial muscle reflex activities and can be used for image pattern recognition. All tests show a strong relationship with ion homeostasis. These devices could be applied to neuromorphic robots and human-machine interfaces.

The 3D Monolithically Integrated Hardware Based Neural System with Enhanced Memory Window of the Volatile and Non-Volatile Devices.

3D neuromorphic hardware system is first demonstrated in neuromorphic application as on-chip level by integrating array devices with CMOS circuits after wafer bonding (WB) and interconnection process. The memory window of synaptic device is degraded after WB and 3 Dimesional (3D) integration due to process defects and thermal stress. To address this degradation, Ag diffusion in materials of Ta2O5 and HfO2 is studied in a volatile memristor, furthermore, the interconnection and gate metal Ru are investigated to reduce defective traps of gate interface in non-volatile memory devices. As a result, a memory window is improved over 106 in both types of devices. Improved and 3D integrated 12 × 14 array devices are identified in the synaptic characteristics according to the change of the synaptic weight from the interconnected Test Element Group (TEG) of the Complementary Metal Oxide Semiconductor (CMOS) circuits. The trained array devices present recognizable image of letters, achieving an accuracy rate of 92% when utilizing a convolutional neural network, comparing the normalized accuracy of 93% achieved by an ideal synapse device. This study proposes to modulate the memory windows up to 106 in an integrated hardware-based neural system, considering the possibility of device degradation in both volatile and non-volatile memory devices demonstrated by the hardware neural system.

Memristive Switching Mechanism in Colloidal InP/ZnSe/ZnS Quantum Dot-Based Synaptic Devices for Neuromorphic Computing.

Quantum dots (QDs) have garnered a significant amount of attention as promising memristive materials owing to their size-dependent tunable bandgap, structural stability, and high level of applicability for neuromorphic computing. Despite these advantageous properties, the development of QD-based memristors has been hindered by challenges in understanding and adjusting the resistive switching (RS) behavior of QDs. Herein, we propose three types of InP/ZnSe/ZnS QD-based memristors to elucidate the RS mechanism, employing a thin poly(methyl methacrylate) layer. This approach not only allows us to identify which carriers (electron or hole) are trapped within the QD layer but also successfully demonstrates QD-based synaptic devices. Furthermore, to utilize the QD memristor as a synapse, long-term potentiation/depression (LTP/LTD) characteristics are measured, resulting in a low nonlinearity of LTP/LTD at 0.1/1. On the basis of the LTP/LTD characteristics, single-layer perceptron simulations were performed using the Extended Modified National Institute of Standards and Technology, verifying a maximum recognition rate of 91.46%.

A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects.

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.

Realization of Multiple Synapse Plasticity by Coexistence of Volatile and Nonvolatile Characteristics of Interface Type Memristor.

Studies on neuromorphic computing systems are becoming increasingly important in the big-data-processing era as these systems are capable of energy-efficient parallel data processing and can overcome the present limitations owing to the von Neumann bottleneck. The Pt/WOx/ITO resistive random-access memory device can be used to implement versatile synapse functions because it possesses both volatile and nonvolatile characteristics. The gradual increase and decrease in the current of the Pt/WOx/ITO device with its uniform resistance state for endurance and retention enables additional synaptic applications that can be controlled using electric pulses. If the volatile and nonvolatile device properties are set through rehearsal and forgetting processes, the device can emulate various synaptic behaviors, such as potentiation and depression, paired-pulse facilitation, post-tetanic potentiation, image training, Hebbian learning rules, excitatory postsynaptic current, and Pavlov's test. Furthermore, reservoir computing can be implemented for applications such as pattern generation and recognition. This emphasizes the various applications of future neuromorphic devices, demonstrating the various favorable characteristics of pulse-enhanced Pt/WOx/ITO devices.

High-Performance Memristive Synapse Composed of Ferroelectric ZnVO-Based Schottky Junction.

In pursuit of realizing neuromorphic computing devices, we demonstrated the high-performance synaptic functions on the top-to-bottom Au/ZnVO/Pt two-terminal ferroelectric Schottky junction (FSJ) device architecture. The active layer of ZnVO exhibited the ferroelectric characteristics because of the broken lattice-translational symmetry, arising from the incorporation of smaller V5+ ions into smaller Zn2+ host lattice sites. The fabricated FSJ devices displayed an asymmetric hysteresis behavior attributed to the ferroelectric polarization-dependent Schottky field-emission rate difference in between positive and negative bias voltage regions. Additionally, it was observed that the magnitude of the on-state current could be systematically controlled by changing either the amplitude or the width of the applied voltage pulses. Owing to these voltage pulse-tunable multi-state memory characteristics, the device revealed diverse synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and versatile rules in spike time-dependent synaptic plasticity. For the pattern-recognition simulation, furthermore, more than 95% accuracy was recorded when using the optimized experimental device parameters. These findings suggest the ZnVO-based FSJ device holds significant promise for application in next-generation brain-inspired neuromorphic computing systems.

In Situ Growth of Wafer-Scale Patterned Graphene and Fabrication of Optoelectronic Artificial Synaptic Device Array Based on Graphene/n-AlGaN Heterojunction for Visual Learning.

The unique optical and electrical properties of graphene-based heterojunctions make them significant for artificial synaptic devices, promoting the advancement of biomimetic vision systems. However, mass production and integration of device arrays are necessary for visual imaging, which is still challenging due to the difficulty in direct growth of wafer-scale graphene patterns. Here, a novel strategy is proposed using photosensitive polymer as a solid carbon source for in situ growth of patterned graphene on diverse substrates. The growth mechanism during high-temperature annealing is elucidated, leading to wafer-scale graphene patterns with exceptional uniformity, ideal crystalline quality, and precise control over layer number by eliminating the release of volatile from oxygen-containing resin. The growth strategy enables the fabrication of two-inch optoelectronic artificial synaptic device array based on graphene/n-AlGaN heterojunction, which emulates key functionalities of biological synapses, including short-term plasticity, long-term plasticity, and spike-rate-dependent plasticity. Moreover, the mimicry of visual learning in the human brain is attributed to the regulation of excitatory and inhibitory post-synapse currents, following a learning rule that prioritizes initial recognition before memory formation. The duration of long-term memory reaches 10 min. The in situ growth strategy for patterned graphene represents the novelty for fabricating fundamental hardware of an artificial neuromorphic system.

Ferroelectric 2D SnS2 Analog Synaptic FET.

In this study, the development and characterization of 2D ferroelectric field-effect transistor (2D FeFET) devices are presented, utilizing nanoscale ferroelectric HfZrO2 (HZO) and 2D semiconductors. The fabricated device demonstrated multi-level data storage capabilities. It successfully emulated essential biological characteristics, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC), Pair-Pulse Facilitation (PPF), and Spike-Timing Dependent Plasticity (STDP). Extensive endurance tests ensured robust stability (107 switching cycles, 105 s (extrapolated to 10 years)), excellent linearity, and high Gmax/Gmin ratio (>105), all of which are essential for realizing multi-level data states (>7-bit operation). Beyond mimicking synaptic functionalities, the device achieved a pattern recognition accuracy of ≈94% on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset when incorporated into a neural network, demonstrating its potential as an effective component in neuromorphic systems. The successful implementation of the 2D FeFET device paves the way for the development of high-efficiency, ultralow-power neuromorphic hardware which is in sub-femtojoule (48 aJ/spike) and fast response (1 µs), which is 104 folds faster than human synapse (≈10 ms). The results of the research underline the potential of nanoscale ferroelectric and 2D materials in building the next generation of artificial intelligence technologies.

Polarization-Dependent Memory and Erasure in Quantum Dots/Graphene Synaptic Devices.

We demonstrate excitatory and inhibitory properties in a single heterostructure consisting of two quantum dots/graphene synaptic elements using linearly polarized monochromatic light. Perovskite quantum dots and PbS quantum dots were used to increase and decrease photocurrent weights, respectively. The polarization-dependent photocurrent was realized by adding a polarizer in the middle of the PbS quantum dots/graphene and perovskite quantum dots/graphene elements. When linearly polarized light passed through the polarizer, both the lower excitatory and upper inhibitory devices were activated, with the lower device with the stronger response dominating to increase the current weight. In contrast, the polarized light was blocked by the polarizer, and the above device was only operated, reducing the current weight. Furthermore, two orthogonal polarizations of light were used to perform the sequential processes of potentiation and habituation. By adjustment of the polarization angle of light, not only the direction of the current weight but also its level was altered.

Internal Resistor Effect of Multilayer-Structured Synaptic Device for Low-Power Operation.

A synaptic device with a multilayer structure is proposed to reduce the operating power of neuromorphic computing systems while maintaining a high-density integration. A simple metal-insulator-metal (MIM)-structured multilayer synaptic device is developed using an 8-inch wafer-based and complementary metal-oxide-semiconductor (CMOS) fabrication process. The three types of MIM-structured synaptic devices are compared to assess their effects on reducing the operating power. The obtained results exhibited low-power operation owing to the inserted layers acting as an internal resistor. The modulated operational conductance level and simple MIM structure demonstrate the feasibility of implementing both low-power operation and high-density integration in multilayer synaptic devices.

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

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

Highly Reliable Performance of Flexible Synaptic Devices Based on PVP-GO QD Nanocomposites Due to the Formation of Directional Filaments.

The metallic conductive filament (CF) model, which serves as an important conduction mechanism for realizing synaptic functions in electronic devices, has gained recognition and is the subject of extensive research. However, the formation of CFs within the active layer is plagued by issues such as uncontrolled and random growth, which severely impacts the stability of the devices. Therefore, controlling the growth of CFs and improving the performance of the devices have become the focus of that research. Herein, a synaptic device based on polyvinylpyrrolidone (PVP)/graphene oxide quantum dot (GO QD) nanocomposites is proposed. Doping GO QDs in the PVP provides a large number of active centers for the reduction of silver ions, which allows, to a certain extent, the growth of CFs to be controlled. Because of this, the proposed device can simulate a variety of synaptic functions, including the transition from long-term potentiation to long-term depression, paired-pulse facilitation, post-tetanic potentiation, transition from short-term memory to long-term memory, and the behavior of the "learning experience". Furthermore, after being bent repeatedly, the devices were still able to simulate multiple synaptic functions accurately. Finally, the devices achieved a high recognition accuracy rate of 89.39% in the learning and inference tests, producing clear digit classification results.

Hierarchical processing enabled by 2D ferroelectric semiconductor transistor for low-power and high-efficiency AI vision system.

The growth of data and Internet of Things challenges traditional hardware, which encounters efficiency and power issues owing to separate functional units for sensors, memory, and computation. In this study, we designed an α-phase indium selenide (α-In2Se3) transistor, which is a two-dimensional ferroelectric semiconductor as the channel material, to create artificial optic-neural and electro-neural synapses, enabling cutting-edge processing-in-sensor (PIS) and computing-in-memory (CIM) functionalities. As an optic-neural synapse for low-level sensory processing, the α-In2Se3 transistor exhibits a high photoresponsivity (2855 A/W) and detectivity (2.91 × 1014 Jones), facilitating efficient feature extraction. For high-level processing tasks as an electro-neural synapse, it offers a fast program/erase speed of 40 ns/50 µs and ultralow energy consumption of 0.37 aJ/spike. An AI vision system using α-In2Se3 transistors has been demonstrated. It achieved an impressive recognition accuracy of 92.63% within 12 epochs owing to the synergistic combination of the PIS and CIM functionalities. This study demonstrates the potential of the α-In2Se3 transistor in future vision hardware, enhancing processing, power efficiency, and AI applications.

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

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

All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems.

Neuromorphic computing, an innovative technology inspired by the human brain, has attracted increasing attention as a promising technology for the development of artificial intelligence systems. This study proposes synaptic transistors with a Li1-xAlxTi2-x(PO4)3 (LATP) layer to analyze the conductance modulation linearity, which is essential for weight mapping and updating during on-chip learning processes. The high ionic conductivity of the LATP electrolyte provides a large hysteresis window and enables linear weight update in synaptic devices. The results demonstrate that optimizing the LATP layer thickness improves the conductance modulation and linearity of synaptic transistors during potentiation and degradation. A 20 nm-thick LATP layer results in the most nonlinear depression (αd = -6.59), whereas a 100 nm-thick LATP layer results in the smallest nonlinearity (αd = -2.22). Additionally, a device with the optimal 100 nm-thick LATP layer exhibits the highest average recognition accuracy of 94.8% and the smallest fluctuation, indicating that the linearity characteristics of a device play a crucial role in weight update during learning and can significantly affect the recognition accuracy.

Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing.

The von Neumann architecture has faced challenges requiring high-fulfillment levels due to the performance gap between its processor and memory. Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of amorphous BN have been insufficiently explored for memory applications. Herein, we fabricated a Pt/BN/TiN device utilizing the resistive switching mechanism to achieve synaptic characteristics in a neuromorphic system. The switching mechanism is investigated based on the I-V curves. Utilizing these characteristics, we optimize the potentiation and depression to mimic the biological synapse. In artificial neural networks, high-recognition rates are achieved using linear conductance updates in a memristor device. The short-term memory characteristics are investigated in depression by controlling the conductance level and time interval.

Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision.

Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.

Helical Nanofiber Photoelectric Synaptic Devices for an Artificial Vision Nervous System.

Inspired by the helical structure and the resultant exquisite functions of biomolecules, helical polymers have received increasing attention. Here, a series of poly(3-hexylthiophene)-block-poly(phenyl isocyanide) (P3HT-b-PPI) copolymers were prepared using a simple one-pot living polymerization method. Interestingly, the P3HT80-b-PPI30 films were found to have a helical nanofiber structure. The corresponding device has superior optoelectronic properties, such as a broadened spectral response range from the visible band to the deep ultraviolet (DUV) and an approximately 5-fold longer carrier decay time after DUV light stimulation. An energy consumption of 1.44 fJ per synaptic event was obtained, which is the lowest energy consumption achieved so far with DUV light stimulation. The encryption and decryption of images are implemented using an array of devices. Finally, a photoreceptor neural pathway was constructed to achieve early warning for the recognition of the display of harmful light. This research provides an effective strategy for the development of a novel optoelectronic synaptic device.