A Comparison Study on Multilayered Barrier Oxide Structure in Charge Trap Flash for Synaptic Operation

Minkyung Kim,Tae-Yeon Seong,Jong-Keuk Park,Jongkil Park,Jaewook Kim,Eunpyo Park,Yeonjoo Jeong,Suyoun Lee,Inho Kim,Joon Young Kwak,In Soo Kim

doi:10.3390/cryst11010070

Minkyung Kim, Tae-Yeon Seong + Show 9 more

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https://doi.org/10.3390/cryst11010070

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Abstract

A synaptic device that contains weight information between two neurons is one of the essential components in a neuromorphic system, which needs highly linear and symmetric characteristics of weight update. In this study, a charge trap flash (CTF) memory device with a multilayered high-κ barrier oxide structure on the MoS2 channel is proposed. The fabricated device was oxide-engineered on the barrier oxide layers to achieve improved synaptic functions. A comparison study between two fabricated devices with different barrier oxide materials (Al2O3 and SiO2) suggests that a high-κ barrier oxide structure improves the synaptic operations by demonstrating the increased on/off ratio and symmetry of synaptic weight updates due to a better coupling ratio. Lastly, the fabricated device has demonstrated reliable potentiation and depression behaviors and spike-timing-dependent plasticity (STDP) for use in a spiking neural network (SNN) neuromorphic system.

Highlights

The artificial intelligence (AI) processor has been one of the most important technologies in the 4th industrial revolution [1,2]
AI hardware consisting of conventional, complementary metal-oxide semiconductor (CMOS) circuits based on the von Neumann architecture have been widely implemented, while the system consumes a quite amount of power due to the von Neumann bottleneck [3,4]
This paper provides a study on an artificial synaptic device, which is one of the essential components of a neural network for building a hardware-based spiking neural network (SNN) system

Summary

Introduction

The artificial intelligence (AI) processor has been one of the most important technologies in the 4th industrial revolution [1,2]. AI hardware consisting of conventional, complementary metal-oxide semiconductor (CMOS) circuits based on the von Neumann architecture have been widely implemented, while the system consumes a quite amount of power due to the von Neumann bottleneck [3,4]. To overcome the von Neumann bottleneck, a neuromorphic computing system has been one of the promising candidates to emulate a human brain that consumes approximately 20 Watts of power. This paper provides a study on an artificial synaptic device, which is one of the essential components of a neural network for building a hardware-based spiking neural network (SNN) system. A neuron acts as a spike generator to transfer a spike signal to the neuron, and a synapse connects two neurons to control the connection strength, which is modified by the biological process called synaptic plasticity in the biological nervous system [5,6]

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