Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware. Issue 3 (27th November 2022)

• Record Type: Journal Article
• Authors: Kim, Geunyoung; Son, Seoil; Song, Hanchan; Jeon, Jae Bum; Lee, Jiyun; Cheong, Woon Hyung; Choi, Shinhyun; Kim, Kyung Min
• Is Part Of: Advanced science. Volume 10:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-27
• Subjects: analog -- charge‐trap -- memristors -- neuromorphic -- self‐rectifying; Science -- Periodicals; 505
• DOI: 10.1002/advs.202205654
• Languages: English
• ISSNs: 2198-3844

Abstract

Abstract: A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta2 O5 /Nb2 O5‐ x /Al2 O3‐ y /Ti CTM stack exhibiting high retention and array‐level uniformity is proposed, allowing a highly reliable selector‐less MCA. It shows high self‐rectifying and nonlinear current‐voltage characteristics below 1 µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 10 5 s at 150 °C, suggesting the device is highly stable for non‐volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self‐rectifying and nonlinear characteristics allow reducing the on‐chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme. Abstract : Pt/Ta2 O5 /Nb2 O5‐ x /Al2 O3‐ y /Ti charge trap memristor exhibiting high retention and array‐level uniformity with self‐rectifying and low operation range is proposed. A novel operation model based on electronic spectroscopy and conduction mechanism analysis is suggested. With the self‐rectifying and nonlinear characteristics, energy consumption decreases by 71% with an optimized programming scheme.