Ultrathin All-Solid-State MoS2-Based Electrolyte Gated Synaptic Transistor with Tunable Organic-Inorganic Hybrid Film.

Electrolyte-gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi-state storage capabilities. To demonstrate high-density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D-based EGSTs has challenges in achieving large-area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large-scale process-compatible, all-solid-state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub-30 nm organic-inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD-based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system-level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%.

Imidazole-based artificial synapses for neuromorphic computing: a cluster-type conductive filament via controllable nanocluster nucleation.

Memristive synapses based on conductive bridging RAMs (CBRAMs) utilize a switching layer having low binding energy with active metals for excellent analog conductance modulation, but the resulting unstable conductive filaments cause fluctuation and drift of the conductance. This tunability-stability dilemma makes it difficult to implement practical neuromorphic computing. A novel method is proposed to enhance the stability and controllability of conductive filaments by introducing imidazole groups that boost the nucleation of Cu nanoclusters in the ultrathin polymer switching layer through the initiated chemical vapor deposition (iCVD) process. It is confirmed that conductive filaments based on nanoclusters with specific gaps are generated in the copolymer medium using this method. Furthermore, by modulating the tunneling gaps, an ultra-wide conductance range of analog tunable conductive filaments is achieved from several hundreds of nS to a few mS with a sub-1 V driving voltage. Through this, both reliable and stable analog switching are achieved with low cycle-to-cycle and device-to-device weight update variations and separable state retention with 32 states. This approach paves the way for the extension of state availability in synaptic devices to overcome the tunability-stability dilemma, which is essential for the synaptic elements in neuromorphic systems.

A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure.

Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity. However, the multistate retention in the switching conductance range for the long-term reliable neuromorphic system has not been achieved using two-dimensional materials-based ECM memristors. In this study, the copper migration-controlled ECM memristor showing excellent multistate retention characteristics in the switching conductance range using molybdenum disulfide (MoS2 ) and aluminum oxide (Al2 O3 ) is proposed. The fabricated device exhibits gradual resistive switching with low switching voltage (<0.5 V), uniform switching (σ/µ âˆ¼ 0.07), and a wide switching range (>12). Importantly, excellent reliabilities with robustness to cycling stress and retention over 104 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al2 O3 layer to the retention characteristic is investigated through filament morphology observation using transmission electron microscopy (TEM) and copper migration component analysis. This study provides a practical approach to developing highly reliable memristors with exceptional switching performance.

Preventing Vanishing Gradient Problem of Hardware Neuromorphic System by Implementing Imidazole-Based Memristive ReLU Activation Neuron.

With advances in artificial intelligent services, brain-inspired neuromorphic systems with synaptic devices are recently attracting significant interest to circumvent the von Neumann bottleneck. However, the increasing trend of deep neural network parameters causes huge power consumption and large area overhead of a nonlinear neuron electronic circuit, and it incurs a vanishing gradient problem. Here, a memristor-based compact and energy-efficient neuron device is presented to implement a rectifying linear unit (ReLU) activation function. To emulate the volatile and gradual switching of the ReLU function, a copolymer memristor with a hybrid structure is proposed using a copolymer/inorganic bilayer. The functional copolymer film developed by introducing imidazole functional groups enables the formation of nanocluster-type pseudo-conductive filaments by boosting the nucleation of Cu nanoclusters, causing gradual switching. The ReLU neuron device is successfully demonstrated by integrating the memristor with amorphous InGaZnO thin-film transistors, and achieves 0.5 pJ of energy consumption based on sub-10 µA operation current and high-speed switching of 650 ns. Furthermore, device-to-system-level simulation using neuron devices on the MNIST dataset demonstrates that the vanishing gradient problem is effectively resolved by five-layer deep neural networks. The proposed neuron device will enable the implementation of high-density and energy-efficient hardware neuromorphic systems.

A Sub-20 nm Organic/Inorganic Hybrid Dielectric for Ultralow-Power Organic Thin-Film Transistor (OTFT) With Enhanced Operational Stability.

Organic/inorganic hybrid materials are utilized extensively as gate dielectric layers in organic thin-film transistors (OTFTs). However, inherently low dielectric constant of organic materials and lack of a reliable deposition process for organic layers hamper the broad application of hybrid dielectric materials. Here, a universal strategy to synthesize high-k hybrid dielectric materials by incorporating a high-k polymer layer on top of various inorganic layers generated by different fabrication methods, including AlOx and HfOx , is presented. Those hybrid dielectrics commonly exhibit high capacitance (>300 nF·cm-2 ) as well as excellent insulating properties. A vapor-phase deposition method is employed for precise control of the polymer film thickness. The ultralow-voltage (<3 V) OTFTs are demonstrated based on the hybrid dielectric layer with 100% yield and uniform electrical characteristics. Moreover, the exceptionally high stability of OTFTs for long-term operation (current change less than 5% even under 30 h of voltage stress at 2.0 MV·cm-1 ) is achieved. The hybrid dielectric is fully compatible with various substrates, which allows for the demonstration of intrinsically flexible OTFTs on the plastic substrate. It is believed that this approach for fabricating hybrid dielectrics by introducing the high-k organic material can be a promising strategy for future low-power, flexible electronics.

A Vertical Silicon Nanowire Based Single Transistor Neuron with Excitatory, Inhibitory, and Myelination Functions for Highly Scalable Neuromorphic Hardware.

A single transistor neuron (1T-neuron) is demonstrated by using a vertically protruded nanowire from an 8 in. silicon (Si) wafer. The 1T-neuron adopts a gate-all-around structure to completely surround the Si nanowire (Si-NW) to make a floating body and allow aggressive downscaling. The Si-NW is composed of an n+ drain at the top, n+ source at the bottom, and p-type floating body at the middle, which are self-aligned vertically. Thus, it occupies a small footprint area. The gate controls an excitatory/inhibitory function. In addition, myelination of a biological neuron that changes membrane capacitance is mimicked by an inherently asymmetric source/drain structure. Two spiking frequencies at the same input current are controlled by whether the neuron is myelinated or unmyelinated. Using the vertical 1T-neuron, pattern recognition is demonstrated with both measurements and semiempirical circuit simulations. Furthermore, handwritten numbers in the MNIST database are recognized with accuracy of 93% by software-based simulations. Applicability of the vertical 1T-neuron to various neural networks is verified, including a single-layer perceptron, multilayer perceptron, and spiking neural network.

Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware.

Cointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neurons and synapses with additional CMOS circuits. Such cointegration can enhance packing density, reduce chip cost, and simplify fabrication procedures. The multistate single-transistor neuron that can control neuronal inhibition and the firing threshold voltage was achieved for an energy-efficient and reliable neural network. Spatiotemporal neuronal functionalities are demonstrated with fabricated single-transistor neurons and synapses. Image processing for letter pattern recognition and face image recognition is performed using experimental-based neuromorphic simulation.

Conductive-bridging random-access memories for emerging neuromorphic computing.

With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal-oxide-semiconductor (CMOS) processes. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on-off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave the way toward further development of large-scale CBRAM array systems.

Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System.

With the advent of artificial intelligence (AI), memristors have received significant interest as a synaptic building block for neuromorphic systems, where each synaptic memristor should operate in an analog fashion, exhibiting multilevel accessible conductance states. Here, we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament. With the quantized conductance states observed in the flexible pV3D3 memristor, analog potentiation and depression characteristics of the memristive synapse are obtained through the growth of atomically thin Cu filament and lateral dissolution of the filament via dominant electric field effect, respectively. The face classification capability of our memristor is evaluated via simulation using an artificial neural network consisting of pV3D3 memristor synapses. These results will encourage the development of soft neuromorphic intelligent systems.