Kim, Guhyun Kornijcuk, Vladimir Kim, Dohun Kim, Inho Hwang, Cheol Seong Jeong, Doo Seok 2024-01-19T20:31:14Z 2024-01-19T20:31:14Z 2021-09-02 2019-04 2072-666X https://pubs.kist.re.kr/handle/201004/120169 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. English MDPI MEMORIES Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array Article 10.3390/mi10040219 1 MICROMACHINES, v.10, no.4 MICROMACHINES 10 4 scie scopus 000467772100005 2-s2.0-85065926230 Chemistry, Analytical Nanoscience & Nanotechnology Instruments & Instrumentation Physics, Applied Chemistry Science & Technology - Other Topics Instruments & Instrumentation Physics Article MEMORIES crossbar array artificial neural network multilayer perceptron resistive random access memory (RRAM) supervised learning