Ultrathin Nitride Ferroic Memory with Large ON/OFF Ratios for Analog In‐Memory Computing
Ding Wang, Ping Wang, Shubham Mondal, Mingtao Hu, Yuanpeng Wu, Tao Ma, Zetian Mi
Abstract
Abstract Computing in the analog regime using nonlinear ferroelectric resistive memory arrays can potentially alleviate the energy constraints and complexity/footprint challenges imposed by digital von Neumann systems. Yet the current ferroelectric resistive memories suffer from either low ON/OFF ratios/imprint or limited compatibility with mainstream semiconductors. Here, for the first time, ferroelectric and analog resistive switching in an epitaxial nitride heterojunction comprising ultrathin (≈5 nm) nitride ferroelectrics, i.e., ScAlN, with potentiality to bridge the gap between performance and compatibility is demonstrated. High ON/OFF ratios (up to 10 5 ), high uniformity, good retention, (<20% variation after > 10 5 s) and cycling endurance (>10 4 ) are simultaneously demonstrated in a metal/oxide/nitride ferroelectric junction. It is further demonstrated that the memristor can provide programmability to enable multistate operation and linear analogue computing as well as image processing with high accuracy. Neural network simulations based on the weight update characteristics of the nitride memory yielded an image recognition accuracy of 92.9% (baseline 96.2%) on the images from Modified National Institute of Standards and Technology. The non‐volatile multi‐level programmability and analog computing capability provide first‐hand and landmark evidence for constructing advanced memory/computing architectures based on emerging nitride ferroelectrics, and promote homo and hybrid integrated functional edge devices beyond silicon.