Variation-Resilient FeFET-Based In-Memory Computing Leveraging Probabilistic Deep Learning

• Published in: IEEE transactions on electron devices Vol. 71; no. 5; pp. 2963 - 2969
• Main Authors: Manna, Bibhas, Saha, Arnob, Jiang, Zhouhang, Ni, Kai, Sengupta, Abhronil
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Bayesian Neural Network (BNN); Co-design; Computational modeling; Datasets; Design techniques; device-algorithm co-design; Device-to-device communication; FeFETs; ferroelectric field-effect transistor (FeFET) crossbar; Ferroelectricity; Field effect transistors; in-memory computing; in-memory computing, variation-aware design; Logic gates; Machine learning; MATHEMATICS AND COMPUTING; Neural networks; New technology; Performance evaluation; Programming; Semiconductor devices; Switches; variation-aware design
• ISSN: 0018-9383; 1557-9646
• DOI: 10.1109/TED.2024.3378223

Reliability issues stemming from device level nonidealities of nonvolatile emerging technologies like ferroelectric field-effect transistors (FeFETs), especially at scaled dimensions, cause substantial degradation in the accuracy of in-memory crossbar-based AI systems. In this work, we present a variation-aware design technique to characterize the device level variations and to mitigate their impact on hardware accuracy employing a Bayesian neural network (BNN) approach. An effective conductance variation model is derived from the experimental measurements of cycle-to-cycle (C2C) and device-to-device (D2D) variations performed on FeFET devices fabricated using 28 nm high-k metal gate technology. The variations were found to be a function of different conductance states within the given programming range, which sharply contrasts earlier efforts where a fixed variation dispersion was considered for all conductance values. Such variation characteristics formulated for three different device sizes at different read voltages were provided as prior variation information to the BNN to yield a more exact and reliable inference. Near-ideal accuracy for shallow networks (MLP5 and LeNet models) on the MNIST dataset and limited accuracy decline by ~3.8%-16.1% for deeper AlexNet models on CIFAR10 dataset under a wide range of variations corresponding to different device sizes and read voltages, demonstrates the efficacy of our proposed device-algorithm co-design technique.