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 and Duygu Kuzum ()
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Jaeseoung Park: University of California San Diego
Ashwani Kumar: University of California San Diego
Yucheng Zhou: University of California San Diego
Sangheon Oh: University of California San Diego
Jeong-Hoon Kim: University of California San Diego
Yuhan Shi: University of California San Diego
Soumil Jain: University of California San Diego
Gopabandhu Hota: University of California San Diego
Erbin Qiu: University of California San Diego
Amelie L. Nagle: Massachusetts Institute of Technology
Ivan K. Schuller: University of California San Diego
Catherine D. Schuman: University of Tennessee
Gert Cauwenberghs: University of California San Diego
Duygu Kuzum: University of California San Diego

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46682-1

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