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

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

doi:10.1038/s41467-024-46682-1

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

Journal Article · 25 April 2024 · Nature Communications

DOI: https://doi.org/10.1038/s41467-024-46682-1 · OSTI ID:2472220

^[1]; Kumar, Ashwani ^[2]; Zhou, Yucheng ^[2]; Oh, Sangheon ^[2]; Kim, Jeong-Hoon ^[2]; Shi, Yuhan ^[2]; Jain, Soumil ^[2]; Hota, Gopabandhu ^[2]; Qiu, Erbin ^[2]; Nagle, Amelie L. ^[3]; Schuller, Ivan K. ^[2];

^[4]; Cauwenberghs, Gert ^[2];

^[2]

Univ. of California, San Diego, La Jolla, CA (United States); OSTI
Univ. of California, San Diego, La Jolla, CA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Univ. of Tennessee, Knoxville, TN (United States)

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.

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Research Organization:: Univ. of California, San Diego, CA (United States); Energy Frontier Research Centers (EFRC) (United States). Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); US Department of the Navy, Office of Naval Research (ONR)

Grant/Contract Number:: SC0019273

OSTI ID:: 2472220

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 15; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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