Journal Article

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

Woo, Kyung Seok, Jang, Yoon Ho, Hwang, Cheol Seong, Han, Janguk, Kim, Woohyun, Kim, Jaehyun

2022

Overview

A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu 0.1 Te 0.9 /HfO 2 /Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization. Designing a computing scheme to solve complex tasks as the big data field proliferates remains a challenge. Here, the authors present a probabilistic bit generation hardware built using the random nature of Cu x Te 1− x /HfO 2 /Pt memristors capable of performing logic gates with invertible mode, showing the expandability to complex logic circuits.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166/987 / 639/301/1005/1008 / Annealing / Big Data / Boolean / Boolean algebra / Computation / Electrodes / Energy / Hafnium oxide / Humanities and Social Sciences / Logic circuits / Memristors / multidisciplinary / Neural networks / Probability theory / Science / Science (multidisciplinary) / Semiconductor research / Statistical analysis / Task complexity