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Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array

Title
Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array
Author
정두석
Keywords
crossbar array; artificial neural network; multilayer perceptron; resistive random access memory (RRAM); supervised learning
Issue Date
2019-04
Publisher
MDPI
Citation
MICROMACHINES, v. 10, NO 4, no. 219
Abstract
An artificial neural network was utilized in the behavior inference of a random crossbar array (10 x 9 or 28 x 27 in size) of nonvolatile binary resistance-switches (in a high resistance state (HRS) or low resistance state (LRS)) in response to a randomly applied voltage array. The employed artificial neural network was a multilayer perceptron (MLP) with leaky rectified linear units. This MLP was trained with 500,000 or 1,000,000 examples. For each example, an input vector consisted of the distribution of resistance states (HRS or LRS) over a crossbar array plus an applied voltage array. That is, for a M x N array where voltages are applied to its M rows, the input vector was M x (N + 1) long. The calculated (correct) current array for each random crossbar array was used as data labels for supervised learning. This attempt was successful such that the correlation coefficient between inferred and correct currents reached 0.9995 for the larger crossbar array. This result highlights MLP that leverages its versatility to capture the quantitative linkage between input and output across the highly nonlinear crossbar array.
URI
https://www.mdpi.com/2072-666X/10/4/219http://repository.hanyang.ac.kr/handle/20.500.11754/111014
ISSN
2072-666X
DOI
10.3390/mi10040219
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > MATERIALS SCIENCE AND ENGINEERING(신소재공학부) > Articles
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