Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

Jaeseoung Park; Ashwani Kumar; Yucheng Zhou; Sangheon Oh; Jeong-Hoon Kim; Yuhan Shi; Soumil Jain; Gopabandhu Hota; Erbin Qiu; Amelie L Nagle; Ivan K Schuller; Catherine D Schuman; Gert Cauwenberghs; Duygu Kuzum

doi:10.1038/s41467-024-46682-1

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

Nat Commun. 2024 Apr 25;15(1):3492. doi: 10.1038/s41467-024-46682-1.

Authors

Affiliations

¹ Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
² Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
³ Department of Physics, University of California San Diego, La Jolla, CA, USA.
⁴ Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.
⁶ Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA. dkuzum@ucsd.edu.

^# Contributed equally.

Abstract

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.