Low‐Conductance and Multilevel CMOS‐Integrated Nanoscale Oxide Memristors. (2nd May 2019)

• Record Type: Journal Article
• Authors: Sheng, Xia; Graves, Catherine E.; Kumar, Suhas; Li, Xuema; Buchanan, Brent; Zheng, Le; Lam, Sity; Li, Can; Strachan, John Paul
• Is Part Of: Advanced Electronic Materials. Volume 5:Number 9(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-02
• Subjects: hardware accelerators -- memristor -- neuromorphic computing -- tantalum oxide; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.201800876
• Languages: English
• ISSNs: 2199-160X

Abstract

Abstract: Using memristors, such as oxide and phase change resistive switches, as tunable resistors to construct analog computing hardware accelerators is gaining keen attention. Such accelerators have demonstrated the potential to significantly outperform digital computers in highly relevant applications such as machine learning and image processing. However, improvements in device‐level performance of memristors, including reducing power consumption and high current–induced metal migration in interconnects, need continued developments. Nanoscaling and complementary metal‐oxide semiconductor (CMOS) integration are also of significant importance in commercialization of such accelerators. Here tantalum oxide memristors scaled down to 25 nm sizes and integrated on CMOS transistor circuits are presented. The memristor conductance is programmable with a 6 order‐of‐magnitude operating range, especially with 3‐bits below 10 µS for low current operation. The stability of such levels and the size scaling of the operating parameters are further studied. These results will aid device engineering of memristors and bolster development of neuromorphic hardware accelerators. Abstract : Improved performance of memristors with very low conductance levels (<10 µS) and very high tunability of conductances across a large on–off ratio (over 6 orders of magnitude ) are demonstrated. This paves the path for many low‐power electronic applications, while the results are attractive especially for highly energy‐efficient neuromorphic computing applications.