Evaluating Spintronic Devices Using The Modular Approach

TL;DR

This paper uses the Modular Approach framework to evaluate spintronic devices, identifying physical and engineering challenges, assessing potential advancements, and exploring their application in probabilistic networks for future computing.

Contribution

It introduces a systematic evaluation method for spintronic devices and demonstrates their potential in probabilistic networks, highlighting challenges and future directions.

Findings

Identified key physical bottlenecks in current spintronic devices.

Showed how material and design improvements can enhance performance.

Illustrated the use of spintronic building blocks in probabilistic networks.

Abstract

Over the past decade a large family of spintronic devices have been proposed as candidates for replacing CMOS for future digital logic circuits. Using the recently developed Modular Approach framework, we investigate and identify the physical bottlenecks and engineering challenges facing current spintronic devices. We then evaluate how systematic advancements in material properties and device design innovations impact the performance of spintronic devices, as a possible continuation of Moore's Law, even though some of these projections are speculative and may require technological breakthroughs. Lastly, we illustrate the use of the Modular Approach as an exploratory tool for probabilistic networks, using superparamagnetic magnets as building blocks for such networks. These building blocks leverage the inherent physics of stochastic spin-torque switching and could provide ultra-compact and efficient hardware for beyond-Boolean computational paradigms.