A hardware Markov chain algorithm realized in a single device for machine learning. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Tian, He; Wang, Xue-Feng; Mohammad, Mohammad; Gou, Guang-Yang; Wu, Fan; Yang, Yi; Ren, Tian-Ling
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 11
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-06644-w
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract There is a growing need for developing machine learning applications. However, implementation of the machine learning algorithm consumes a huge number of transistors or memory devices on-chip. Developing a machine learning capability in a single device has so far remained elusive. Here, we build a Markov chain algorithm in a single device based on the native oxide of two dimensional multilayer tin selenide. After probing the electrical transport in vertical tin oxide/tin selenide/tin oxide heterostructures, two sudden current jumps are observed during the set and reset processes. Furthermore, five filament states are observed. After classifying five filament states into three states of the Markov chain, the probabilities between each states show convergence values after multiple testing cycles. Based on this device, we demo a fixed-probability random number generator within 5% error rate. This work sheds light on a single device as one hardware core with Markov chain algorithm. Despite the need to develop resistive random access memory (RRAM) devices for machine learning, RRAM array-based hardware methods for algorithm require external electronics. Here, the authors realize a Markov chain algorithm in a single 2D multilayer SnSe device without external electronics.