Novel Ferroelectric Tunnel FinFET based Encryption-embedded Computing-in-Memory for Secure AI with High Area-and Energy-Efficiency

In this work, novel ferroelectric tunnel FET (FeTFET) is proposed and experimentally demonstrated to implement encryption-embedded computing-in-memory with non-volatility (nvCIM), enabling both in-situ key authentication and multiply-accumulate (MAC) operation with high area-and energy-efficiency. For the first time, XOR-cipher for weight encryption is merged into XNOR-based MAC, eliminating explicit decryption process and simplifying the local multiplication directly on the encrypted weight with XNOR-operator. Furthermore, by exploiting and modulating the non-volatile ferroelectric polarization for encrypted weight storage and the unique feature of ambipolar tunneling current for input, the XNOR operator can be realized by the fabricated FeTFET based on 14-nm FinFET technology node with only one transistor, enabling the encryption-embedded MAC with multilevel weight without the need of extra decryption circuitry or complementary encrypted weight storage. Based on the proposed FeTFET-based encryption-embedded nVCIM design, security-enhanced neural network inference and one-shot learning are demonstrated with high energy efficiency, showing its great potential for secure AI.