# Stack-Engineered Mode Selection in PtMn/(Co/Pd)n Multilayers Enables Deterministic Analog Spin–Orbit Torque Synapses

## TL;DR

This paper shows how adjusting the structure of a material can control the switching behavior of spintronic devices for efficient neuromorphic computing.

## Contribution

The study introduces stack-engineered mode selection to enable deterministic analog spin–orbit torque synapses.

## Key findings

- For nCo/Pd ≤ 7, binary switching transitions to analog behavior through electrical conditioning.
- For nCo/Pd ≥ 8, analog switching is stabilized without additional processing.
- Hybrid tuning improves neuromorphic classification accuracy to over 97%.

## Abstract

Spin–orbit-torque (SOT) devices that support both
binary
and analog switching can bridge spintronic memory and neuromorphic
computing, provided the switching mode can be deliberately assigned.
Here, we demonstrate that in PtMn/(Co/Pd)­n multilayers, the Co/Pd
repeat number, nCo/Pd, serves as a material parameter that
determines the reversal mechanism and switching mode. For nCo/Pd ≤ 7, magnetization reversal is governed by nucleation followed
by domain-wall propagation, resulting in binary switching that can
undergo a transition to analog behavior through electrical conditioning.
For nCo/Pd ≥ 8, increased structural modulation
in the as-deposited state suppresses domain-wall propagation and stabilizes
nucleation-dominated analog switching without additional processing.
Applying controlled current conditioning to these high-nCo/Pd stacks produces a hybrid state with smoother long-term potentiation
and depression, an expanded number of intermediate states, and neuromorphic
classification accuracy exceeding 97%. These results establish stack
design and hybrid tuning as scalable strategies for energy-efficient
analog SOT synapses in neuromorphic hardware.