The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at device level to enable novel compute-in-memory (CIM) operations. A key challenge in construction of CIM architectures is the conflicting trade-off between the performance and their flexibility for various essential data operations. Here, we present a transistor-free CIM architecture that permits storage, search and neural network operations on sub-50nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). Our circuit designs and devices can be directly integrated on top of Silicon microprocessors in a scalable process. By leveraging the field-programmability, non-volatility and non-linearity of FeDs, search operations are demonstrated with a cell footprint < 0.12 um2 when projected onto 45-nm node technology. We further demonstrate neural network operations with 4-bit operation using FeDs. Our results highlight FeDs as candidates for efficient and multifunctional CIM platforms.
@article{arxiv.2202.05259,
title = {Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric Diodes},
author = {Xiwen Liu and John Ting and Yunfei He and Merrilyn Mercy Adzo Fiagbenu and Jeffrey Zheng and Dixiong Wang and Jonathan Frost and Pariasadat Musavigharavi and Giovanni Esteves and Kim Kisslinger and Surendra B. Anantharaman and Eric A. Stach and Roy H. Olsson and Deep Jariwala},
journal= {arXiv preprint arXiv:2202.05259},
year = {2024}
}