Ferroelectric Diodes for Reconfigurable Compute-In-Memory

The deluge of sensors and data generating devices has driven a paradigm shift in modern<br/>computing from arithmetic-logic centric to data centric processing. At a hardware level, this<br/>presents an urgent need to integrate dense, high-performance and low-power memory units<br/>with Si logic-processor units. However, data-heavy problems such as search and pattern<br/>matching also require paradigm changing innovations at the circuit and architecture level to<br/>enable compute in memory (CIM) operations. CIM architectures that combine data storage yet<br/>concurrently offer low-delay and small footprint are highly sought after but have not been<br/>realized. Here, we present Aluminum Scandium Nitride (AlScN) ferroelectric diode (FeD)<br/>memristor devices that allow for storage, search and neural network-based pattern recognition<br/>in a transistor-free architecture. Our devices can be directly integrated on top of Si processors<br/>in a scalable, back-end-of-line process. We leverage the field-programmability, non-volatility<br/>and non-linearity of FeDs to demonstrated circuit blocks that can support search operations in-<br/>situ memory with search delay times &lt; 0.1 ns and a cell footprint &lt; 0.12 µm 2 . In addition, we<br/>demonstrate matrix multiplication operations with 4-bit operation of the FeDs. Our results<br/>highlight FeDs as promising candidates for fast, efficient, and multifunctional CIM platforms.