First demonstration of in-memory computing crossbar using multi-level Cell FeFET

Taha Soliman (), Swetaki Chatterjee, Nellie Laleni, Franz Müller, Tobias Kirchner, Norbert Wehn, Thomas Kämpfe (), Yogesh Singh Chauhan and Hussam Amrouch ()
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Taha Soliman: Robert Bosch GmbH
Swetaki Chatterjee: University of Stuttgart
Nellie Laleni: Fraunhofer IPMS
Franz Müller: Fraunhofer IPMS
Tobias Kirchner: Robert Bosch GmbH
Norbert Wehn: RPTU Kaiserslautern-Landau
Thomas Kämpfe: Fraunhofer IPMS
Yogesh Singh Chauhan: Indian Institute of Technology Kanpur
Hussam Amrouch: Technical University of Munich; TUM School of Computation, Information and Technology; Chair of AI Processor Design; Munich Institute of Robotics and Machine Intelligence (MIRMI)

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Advancements in AI led to the emergence of in-memory-computing architectures as a promising solution for the associated computing and memory challenges. This study introduces a novel in-memory-computing (IMC) crossbar macro utilizing a multi-level ferroelectric field-effect transistor (FeFET) cell for multi-bit multiply and accumulate (MAC) operations. The proposed 1FeFET-1R cell design stores multi-bit information while minimizing device variability effects on accuracy. Experimental validation was performed using 28 nm HKMG technology-based FeFET devices. Unlike traditional resistive memory-based analog computing, our approach leverages the electrical characteristics of stored data within the memory cell to derive MAC operation results encoded in activation time and accumulated current. Remarkably, our design achieves 96.6% accuracy for handwriting recognition and 91.5% accuracy for image classification without extra training. Furthermore, it demonstrates exceptional performance, achieving 885.4 TOPS/W–nearly double that of existing designs. This study represents the first successful implementation of an in-memory macro using a multi-state FeFET cell for complete MAC operations, preserving crossbar density without additional structural overhead.

Date: 2023
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DOI: 10.1038/s41467-023-42110-y

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