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A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
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Related Experiment Video

Updated: Jan 12, 2026

A Method for Growing Bio-memristors from Slime Mold

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

Memristor-based adaptive analog-to-digital conversion for efficient and accurate compute-in-memory.

Haiqiao Hong¹, Zhiyuan Du¹, Mingrui Jiang¹

¹Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.

Nature Communications

|November 6, 2025

Summary

This study introduces an adaptive memristor-based analog-to-digital converter for neural network acceleration. It significantly improves energy efficiency and reduces area, enabling practical compute-in-memory systems.

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Last Updated: Jan 12, 2026

A Method for Growing Bio-memristors from Slime Mold

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

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In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Published on: May 13, 2020

Area of Science:

Materials Science
Computer Engineering
Artificial Intelligence

Background:

Compute-in-memory (CIM) technology accelerates neural networks but is hindered by analog-to-digital converters (ADCs).
Existing ADCs are often inflexible and consume significant resources, limiting CIM system efficiency.
Novel ADC designs are crucial for unlocking the full potential of CIM for AI applications.

Purpose of the Study:

To develop a memristor-based ADC with adaptive quantization for diverse output distributions.
To enhance the performance and efficiency of neural network acceleration in CIM systems.
To overcome the limitations of traditional ADCs in resource-intensive AI hardware.

Main Methods:

Designed a memristor-based ADC utilizing analog content-addressable memory cells.
Implemented programmable overlapped boundaries for optimized quantization thresholds.
Validated performance using CIFAR-10 (VGG8) and ImageNet (ResNet18) datasets with adaptive quantization and super-resolution strategies.

Main Results:

Achieved 89.55% accuracy on CIFAR-10 (VGG8) at 5-bit adaptive quantized precision.
Demonstrated competitive performance on ImageNet (ResNet18) despite experimental memristor variations.
Showcased a 15.1x improvement in energy efficiency and a 12.9x reduction in area compared to state-of-the-art ADCs.

Conclusions:

The proposed memristor-based ADC offers a paradigm for efficient and accurate signal quantization in CIM systems.
Adaptive quantization and super-resolution strategies enhance accuracy and robustness under device variations.
Significant energy and area savings were achieved, paving the way for practical, high-performance CIM solutions.