Memristive-based in-memory computing: from device to large-scale CMOS integration. Issue 2 (18th November 2021)

• Record Type: Journal Article
• Authors: Perez-Bosch Quesada, E; Perez, E; Mahadevaiah, M Kalishettyhalli; Wenger, C
• Is Part Of: Neuromorphic computing and engineering. Volume 1:Issue 2(2021)
• Publication Date: 2021-11-18
• Subjects: memristors -- wafer-scale -- in-memory computing -- compact modeling; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac2cd4
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: With the rapid emergence of in-memory computing systems based on memristive technology, the integration of such memory devices in large-scale architectures is one of the main aspects to tackle. In this work we present a study of HfO 2 -based memristive devices for their integration in large-scale CMOS systems, namely 200 mm wafers. The DC characteristics of single metal–insulator–metal devices are analyzed taking under consideration device-to-device variabilities and switching properties. Furthermore, the distribution of the leakage current levels in the pristine state of the samples are analyzed and correlated to the amount of formingless memristors found among the measured devices. Finally, the obtained results are fitted into a physic-based compact model that enables their integration into larger-scale simulation environments.