Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

Automated summary

This study demonstrates an in-memory sensing and computing architecture using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is achieved through the multiplication of light power and photoresponsivity using the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, allowing for multiple distinguishable weight states. This architecture enables image recognition without the need for external memory and computing units, paving the way for low-energy, low-latency, and reduced hardware overhead computing.