Integrate-and-Fire Neuron With Li-Based Electrochemical Random Access Memory Using Native Linear Current Integration Characteristics

Neuromorphic computing has gained a considerable research interest due to its potential in realizing highly efficient parallel computations. However, the existing neuromorphic architectures face various drawbacks. In this study, we present an integrate-and-fire (I&F) neuron using a Li-based electrochemical random access memory (Li-ECRAM) to achieve exceptional area efficiency and low-power neuromorphic computing. The proposed Li-ECRAM neuron employs a significantly reduced number of transistors when compared to other novel nonvolatile memory-based I&F neurons due to linear current integration characteristics and a high linear conductance response to the input current. As the integration-type Li-ECRAM is linear, it eliminates the requirement of a nonlinear compensating circuit. Therefore, a Li-ECRAM-based neuron has a simple structure comprising Li-ECRAM, reset transistor, inverter, and pulse generator. Furthermore, we also evaluate the operation speed and energy consumption of the proposed neuron, demonstrating the potential for high-speed and low-power operation. The proposed neuron can be applied in large-scale neuromorphic hardware applications due to the scalability and low energy consumption of Li-ECRAM. IEEE11Nsciescopu

Nonvolatile Frequency-Programmable Oscillator With NbO₂ and Li-Based Electro-Chemical Random Access Memory for Coupled Oscillators-Based Temporal Pattern Recognition System

In this letter, we propose a compact frequency programmable oscillator with an NbO2-based insulator-metal transition (IMT) device and three-terminal Li-based electro-chemical RAM (Li-ECRAM) for coupled oscillators-based temporal pattern recognition system. Owing to the non-volatility, multilevel characteristics, and linear conductance modulation of Li-ECRAM, our proposed oscillator exhibits a large number of programmable frequencies (45) and high controllability by applying pulses to the Li-ECRAM to attain the target frequency. Furthermore, we demonstrated injection locking phenomenon in our proposed oscillators, which can be utilized for the frequency detection of the injected signal. Finally, we simulated four-coupled oscillators system for the frequency classification of the input temporal signal with multiple frequencies and amplitude noise. These results demonstrate the feasibility of a temporal pattern recognition system composed of our proposed compact frequency-programmable oscillators.11Nsciescopu

OTS-based Analog-to-Stochastic Converter for Fully-Parallel Weight Update in Cross-point Array Neural Networks

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Excellent Synapse Characteristics of 50 nm Vertical Transistor with WOx channel for High Density Neuromorphic system

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Improved On-chip Training Efficiency at Elevated Temperature and Excellent Inference Accuracy with Retention (> 108 s) of Pr0.7Ca0.3MnO3-x ECRAM Synapse Device for Hardware Neural Network

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Impact of Operating Temperature on Pattern Recognition Accuracy of Resistive Array-Based Hardware Neural Networks

In hardware neural networks (HNNs), different operating temperatures cause variation in conductance of resistive arrays, and they can significantly distort the information of the synaptic weights, leading to a considerable loss in pattern recognition accuracy. In this study, a WOx-based resistive device is characterized with varying ambient temperatures, and 1k-bit synapse arrays are evaluated. A systematic analysis of the impact of operating temperature on the array-based HNNs is executed using neural network simulations. Moreover, we propose a temperature compensator (TC) that can mitigate anomalous array behavior without modifying the readout circuitry. Our results have demonstrated successful accuracy recovery of the array-based HNN over a wide range of operating temperatures.11Nsciescopu

High-Speed Ternary CMOS Inverter by Monolithic Integration of NbO2 Threshold Switch with MOSFET

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Microstructural engineering in interface-type synapse device for enhancing linear and symmetric conductance changes

The origins of the nonlinear and asymmetric synaptic characteristics of TiOx-based synapse devices were investigated. Based on the origins, a microstructural electrode was utilized to improve the synaptic characteristics. Under an identical pulse bias, a TiOx-based synapse device exhibited saturated conductance changes, which led to nonlinear and asymmetric synaptic characteristics. The formation of an interfacial layer between the electrode and TiOx layer, which can limit consecutive oxygen migration and chemical reactions, was considered as the main origin of the conductance saturation behavior. To achieve consecutive oxygen migration and chemical reactions, structural engineering was utilized. The resultant microstructural electrode noticeably improved the synaptic characteristics, including the unsaturated, linear, and symmetric conductance changes. These synaptic characteristics resulted in the recognition accuracy significantly increasing from 38% to 90% in a neural network-based pattern recognition simulation.11Nsciescopu