Straintronic magnetic tunnel junctions for analog computation: A perspective

TL;DR

Straintronic magnetic tunnel junctions (s-MTJs) offer continuous, analog resistance modulation via strain, enabling efficient analog computation and neural network applications, surpassing memristors and domain wall synapses in linearity and analog behavior.

Contribution

This paper presents the concept and potential of s-MTJs for analog computing, highlighting their linear transfer characteristics and advantages over existing memristive devices.

Findings

s-MTJs can be continuously tuned with gate voltage

They exhibit a linear conductance region suitable for analog computation

s-MTJs outperform memristors and domain wall synapses in linearity

Abstract

The straintronic magnetic tunnel junction (s-MTJ) is an MTJ whose resistance state can be changed continuously or gradually from high to low with a gate voltage that generates strain the magnetostrictive soft layer. This unusual feature, not usually available in MTJs that are switched abruptly with spin transfer torque, spin-orbit torque or voltage-controlled-magnetic-anisotropy, enables many analog applications where the typically low tunneling magneto-resistance ratio of MTJs (on/off ratio of the switch) and the relatively large switching error rate are not serious impediments unlike in digital logic or memory. More importantly, the transfer characteristic of a s-MTJ (conductance versus gate voltage) always sports a linear region that can be exploited to implement analog arithmetic, vector matrix multiplication and linear synapses in deep learning networks very effectively. In these applications, the s-MTJ is actually superior to the better known memristors and domain wall synapses which do not exhibit the linearity and/or the analog behavior.