Ferroelectric Digital In-Memory Computing for Scalable, Reliable, and Efficient Similarity Computation

Classification-based learning in deep neural networks, particularly few-shot learning, demands efficient similarity metrics such as Hamming distance. Conventional architectures suffer from high energy overheads due to frequent data movement between memory and processing units, hindering scalability. In-memory computing addresses this by integrating computation within memory, yet analog-based systems rely on power-hungry analog-to-digital converters (ADCs) and face scalability challenges due to device variability, especially in emerging memories. This work presents a fully digital Ferroelectric FET (FeFET)-based Logic-in-Memory (LiM) XOR cell, designed using GlobalFoundries’ 28 nm technology, eliminating ADCs and ensuring robust, energy-efficient, and scalable operation. Our 2T FeFET XOR cell, applied to 4096-bit Hamming distance calculations, achieves $23\times $ lower energy, $3\times $ faster latency, and $14\times $ area reduction over state-of-the-art designs. Delivering 2337 Gsamples/(s $\cdot $ W $\cdot $ mm 2 ) — a $300\times $ improvement — this architecture offers a compelling solution for energy-efficient, reliable, and scalable AI hardware, driving sustainable computing.