Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric Diodes

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 the device level to enable novel compute-in memory (CIM) operations. A key challenge in the 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-50 nm 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, nonvolatility, and nonlinearity of FeDs, search operations are demonstrated with a cell footprint < 0.12 mu m2 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.

Automated summary

The deluge of sensors and data generating devices has driven a paradigm shift in modern computing, resulting in a data-centric processing approach. In this study, we present a transistor-free CIM architecture that allows storage, search, and neural network operations on sub-50 nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). By leveraging the programmability, nonvolatility, and nonlinearity of FeDs, we demonstrate efficient search operations and neural network operations, highlighting FeDs as promising candidates for multifunctional CIM platforms.