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Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

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Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array.

Yuanquan Huang^1,2,3, Qiqiao Wu^4,5, Tiancheng Gong^6,7

¹State Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China.

Nature Communications

|August 28, 2025

Summary

This summary is machine-generated.

Fe-diode devices offer a stable, high-frequency entropy source ideal for edge artificial intelligence (AI) systems. These devices enable efficient, accurate AI computations even under extreme temperatures and high frequencies.

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Area of Science:

Materials Science
Artificial Intelligence
Device Physics

Background:

Edge AI systems demand high-frequency operation, challenging traditional entropy sources that degrade with frequency.
Existing entropy sources struggle with the stability required for edge AI under varying temperatures and frequencies.

Purpose of the Study:

To investigate the suitability of Fe-diode devices as a stable, high-frequency entropy source for edge AI.
To experimentally implement a Bayesian neural network using Fe-diode devices on silicon-based chips.

Main Methods:

Characterization of Fe-diode device noise density across varying read voltages, frequencies, and temperatures.
Experimental implementation of a hierarchical Bayesian neural network utilizing a 3D 16-layer Fe-diode array.
Demonstration of a unified entropy source and 4-state synapse architecture.

Main Results:

Fe-diode devices exhibit stable noise density, modifiable by read voltage, across high frequencies and temperature fluctuations.
The implemented Bayesian neural network achieved high recognition accuracy.
The system demonstrated high area efficiency and a wide working temperature range.

Conclusions:

Fe-diode devices are physically suitable for edge AI applications requiring high frequencies and environmental stability.
The developed Fe-diode-based Bayesian neural network offers low-energy in-situ training and high performance for edge AI.