Various Threshold Switching Devices for Integrate and Fire Neuron Applications

Abstract

To implement a SNN using a hardware system, an integrate and fire (I&F) neuron is commonly adopted as a spiking neuron owing to its simplicity. An I&F neuron integrates the input synaptic current and the membrane potential is charged, as shown in Figure 1a. When the membrane potential reaches the threshold voltage of the neuron, the neuron generates spikes to the next synapse layer and resets the membrane potential. Unfortunately, it is becoming burdensome to use conventional CMOS-based neurons in massive neuromorphic hardware due to their large areas and high power consumption. [6] In this regard, volatile thershold switching (TS) devices [7][8][9][10][11] and nonvolatile memory such as resistive random access memory (RRAM) , [12] phase change random access memory (PRAM), [13] ferromagnetic material, [14] and floating body transistor [15] based I&F neurons have been reported to overcome the limitations of conventional CMOS-based neurons. In nonvolatile memory device based I&F neurons wherein the memory device is used for integrating the input synaptic current, an additional circuit is required to return memory device to its initial state in the reset process of the neuron. However, in a TS-based I&F neuron, due to the volatile voltage hysteric switch characteristic of the TS device, a self-reset process is performed without a reset circuit. Thus, it enables the realization of a compact and low power consumption neuron. Although many TS-based I&F neurons have been studied, only the operations of I&F neuron and their biological plausibility have been reported. However, it is necessary to study and understand the correlation between the switching parameter of a TS device and the neuron characteristics for practical application of TS-based I&F neurons in various SNN-based hardware.Therefore, in this work, we investigated the effect of the switching parameters of the TS devices on the characteristics of TS-based I&F neurons through electrical measurements and computational simulation of three different types of neurons using a NbO 2 -based insulator-to-metal transition device (IMT), [16] a B-Te-based ovonic threshold switching (OTS) device, [17] and a Ag/HfO 2 -based atomic-switching TS device. [18] In addition, we confirmed the feasibility of TS-based neuron by simulating SNN, which converted from analog-based ANN prelearned by backpropagation.This study demonstrates an integrate and fire (I&F) neuron using threshold switching (TS) devices to implement spike-based neuromorphic system. An I&F neuron can be realized using the hysteric voltage switch characteristics of a TS device. To investigate the effects of various TS devices on neuron behavior, neurons are compared using three different types of TS device: NbO 2 -based insulator-to-metal transition (IMT) device, B-Te-based ovonic threshold switching device, and Ag/HfO 2 -based atomic-switching TS device. The results show that the off-state resistance and switching time of the TS devices determine the leaky/nonleaky characteristics and types of activation function ...

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