电阻随机存取存储器
横杆开关
神经形态工程学
重置(财务)
超调(微波通信)
记忆电阻器
计算机科学
稳健性(进化)
材料科学
光电子学
数组数据结构
电子工程
电气工程
电压
工程类
基因
机器学习
经济
化学
生物化学
电信
人工神经网络
金融经济学
作者
Sungjoon Kim,Jinwoo Park,Tae‐Hyeon Kim,Kyungho Hong,Yeongjin Hwang,Byung‐Gook Park,Hyungjin Kim
标识
DOI:10.1002/aisy.202100273
摘要
To apply resistive random‐access memory (RRAM) to the neuromorphic system and improve performance, each cell in the array should be able to operate independently by reducing device variation. In addition, it is necessary to lower the operating current of the RRAM cell and enable gradual switching characteristics to mimic the low‐energy operations of biological. In most filamentary RRAMs, however, overshoot current occurs in the forming stage, and the RRAM shows large device variation, high operating current, and abrupt set and reset switching characteristics. Herein, the shortcomings occurring in the forming stage are overcome by introducing and optimizing an overshoot suppression layer. Consequently, the RRAM exhibits gradual switching characteristics both in the set and reset regions, thereby enabling implementation of 4‐bit multilevel operation. In addition, the forming step can be easily performed in a 16 × 16 crossbar array owing to its self‐compliance characteristics without disturbing neighboring cells in the array. The tuning and vector–matrix multiplication (VMM) operations are also experimentally verified in the array. Finally, classification performance with off‐chip training is compared in terms of accuracy and robustness to tuning tolerance depending on the number of bits of the implemented multiconductance levels.
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