Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 59 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 40 tok/s Pro
GPT-5 High 27 tok/s Pro
GPT-4o 104 tok/s Pro
Kimi K2 195 tok/s Pro
GPT OSS 120B 467 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices and Circuits for In-Memory Computing (2010.13879v1)

Published 26 Oct 2020 in cond-mat.mes-hall

Abstract: There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction (MTJ) memory elements can be used for computation by manipulating a domain wall (DW), a transition region between magnetic domains. But, these devices have suffered from challenges: spin transfer torque (STT) switching of a DW requires high current, and the multiple etch steps needed to create an MTJ pillar on top of a DW track has led to reduced tunnel magnetoresistance (TMR). These issues have limited experimental study of devices and circuits. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction (DW-MTJ) in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy (PMA), spin-orbit torque (SOT) switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 164%, resistance-area product RA = 31 {\Omega}-{\mu}m2, close to the RA of the unpatterned film, and lower switching current density compared to using spin transfer torque. A two-device circuit shows bit propagation between devices. Device initialization variation in switching voltage is shown to be curtailed to 7% by controlling the DW initial position, which we show corresponds to 96% accuracy in a DW-MTJ full adder simulation. These results make strides in using MTJs and DWs for in-memory and neuromorphic computing applications.

Citations (43)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.