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Related Experiment Video

Updated: May 13, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

Strategies of high-accuracy memristor-based analogue computing in memory for artificial intelligence.

Zhixing Jiang¹, Han Zhao¹, Jianshi Tang²

¹School of Integrated Circuits, Beijing Advanced Innovation Center for Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.

Nature Materials

|May 11, 2026

Summary

Memristor-based computing in memory (CIM) promises efficient AI but faces accuracy challenges. This review explores error sources and solutions for high-accuracy analogue CIM systems.

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Area of Science:

Materials Science
Computer Engineering
Artificial Intelligence

Background:

Analogue computing in memory (CIM) using memristors offers significant energy efficiency and performance gains for AI.
However, achieving high accuracy in analogue CIM is challenging due to noise sensitivity and device non-idealities causing computational errors.

Purpose of the Study:

To dissect computing error sources in memristor-based analogue CIM across all hierarchical levels.
To evaluate strategies for minimizing these errors and enabling high-accuracy analogue CIM.
To provide a roadmap for the practical deployment of analogue CIM technology.

Main Methods:

Comprehensive review of error sources from memristor devices and arrays to system architecture and algorithms.
Evaluation of material and device innovations for error mitigation.
Analysis of array-level techniques and algorithm-architecture co-design frameworks.

Main Results:

Identified key error contributors across the analogue CIM hierarchy.
Highlighted material, device, array, and co-design strategies for enhancing accuracy.
Dissected the trade-offs between computing accuracy and implementation costs.

Conclusions:

Material and device innovations, alongside advanced co-design strategies, are crucial for high-accuracy analogue CIM.
Addressing the accuracy-cost trade-off is essential for transitioning analogue CIM from prototypes to large-scale AI applications.
This review provides a roadmap for overcoming current limitations and accelerating the adoption of memristor-based analogue CIM.