A Novel Ambipolar Ferroelectric Tunnel FINFET Based Computing-in-Memory for Quantized Neural Networks with High Area- and Energy-Efficiency

In this work, for the first time, a novel ambipolar ferroelectric tunnel FET (FeTFET) based computing-in-memory (CIM) scheme is proposed and experimentally demonstrated for quantized neural networks (QNNs) with high area- and energy-efficiency. By leveraging non-monotonic transfer characteristic of ambipolar tunnel FET (TFET) for signed multiplication and utilizing the nonvolatile ferroelectric gate modulation for weight storage, the signed CIM cell with weight precision expansion can be implemented by one single FeTFET device based on 14-nm technology node platform. Based on the proposed FeTFET-based CIM design, QNNs for typical pattern recognition tasks are demonstrated with high accuracy and energy efficiency, showing its significant potential for edge AI applications.