# Synergistically Modulating Conductive Filaments in Ion‐Based Memristors for Enhanced Analog In‐Memory Computing

## TL;DR

This paper introduces a memristor design that improves analog in-memory computing by better controlling ion movement and filament formation.

## Contribution

A novel memristor using oxygen-rich SnO2 nanoflowers to synergistically modulate conductive filaments and reduce ion migration issues.

## Key findings

- The memristor achieves a high on/off ratio of >10^6 and 10-year memory retention.
- It demonstrates low switching variability (6.85%) and multiple synaptic functions for neuromorphic computing.
- The device achieves ≥91.39% image recognition accuracy using symmetric analog weight updating.

## Abstract

Memristors offer a promising solution to address the performance and energy challenges faced by conventional von Neumann computer systems. Yet, stochastic ion migration in conductive filament often leads to an undesired performance tradeoff between memory window, retention, and endurance. Herein, a robust memristor based on oxygen‐rich SnO2 nanoflowers switching medium, enabled by seed‐mediated wet chemistry, to overcome the ion migration issue for enhanced analog in‐memory computing is reported. Notably, the interplay between the oxygen vacancy (Vo) and Ag ions (Ag+) in the Ag/SnO2/p++‐Si memristor can efficiently modulate the formation and abruption of conductive filaments, thereby resulting in a high on/off ratio (>106), long memory retention (10‐year extrapolation), and low switching variability (SV = 6.85%). Multiple synaptic functions, such as paired‐pulse facilitation, long‐term potentiation/depression, and spike‐time dependent plasticity, are demonstrated. Finally, facilitated by the symmetric analog weight updating and multiple conductance states, a high image recognition accuracy of ≥ 91.39% is achieved, substantiating its feasibility for analog in‐memory computing. This study highlights the significance of synergistically modulating conductive filaments in optimizing performance trade‐offs, balancing memory window, retention, and endurance, which demonstrates techniques for regulating ion migration, rendering them a promising approach for enabling cutting‐edge neuromorphic applications.

This study leverages the synergy of modulating conductive filament to render a significant breakthrough in memory window, extending up to 1 million, and retention for up to 10 years, fostering advanced paradigms in bionics. These results underscore the judicious regulation of ion migration as a viable route to high‐performance memristors for neuromorphic computing applications.