Ovonic switches enable energy-efficient dendrite-like computing

TL;DR

This paper demonstrates that Ovonic threshold switches can perform complex logic, edge detection, and neuromorphic functions with high energy efficiency, mimicking biological dendrites for advanced computing.

Contribution

It introduces a novel Ovonic switch-based primitive capable of complex logic and image processing, enabling energy-efficient neuromorphic computing.

Findings

Single Ovonic switch performs Boolean logic and XOR operations.

Network of switches can emulate half and full adders.

Significantly improved energy efficiency over digital counterparts.

Abstract

Over the last decade, dendrites within individual biological neurons, which were previously thought to generally perform information pooling and networking, have now been shown to express complex temporal dynamics, Boolean-like logic, arithmetic, signal discrimination, and edge detection for image and sound recognition. Mimicking this rich functional density could offer a powerful primitive for neuromorphic computing, which has sought to replace the aging digital computing paradigms using biological inspirations. Here, using electrically driven Ovonic threshold switching in Sb-Te-doped GeSe, we demonstrate a single two-terminal component capable of self-sustained dynamics and universal Boolean logic, in addition to XOR operations (which is traditionally thought to require a network of active components). We then employ logic-driven dynamics in a single component to detect and estimate the gradients of edges in images, a task that otherwise requires elaborate circuits. A network of Ovonic switches exhibits properties of a half adder and a full adder, in addition to discriminative logic accommodating inhibitory and excitatory signals. We show that this computational primitive is not only seemingly simpler, but also offers many orders of magnitude improved energy efficiency compared to prevailing digital solutions. As such, this work paves the path for potentially emulating dendrites for efficient post-digital neuromorphic computing.