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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

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Published on: September 5, 2012

Programmable chalcogenide-based all-optical deep neural networks.

Ting Yu Teo¹, Xiaoxuan Ma², Ernest Pastor³

¹Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.

Nanophotonics (Berlin, Germany)

|December 5, 2024

Summary

This summary is machine-generated.

This study presents an all-optical perceptron using chalcogenide materials for ultrafast neural network operations. The device utilizes a nonlinear activation function for high-speed, energy-efficient optical classifications exceeding 1 GHz.

Keywords:

all-optical deep neural network chalcogenide reconfigurable photonics ultra-fast dynamic response of phase change material

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Area of Science:

Photonics and Optical Engineering
Materials Science
Artificial Intelligence Hardware

Background:

All-optical computing offers potential for high-speed information processing.
Developing efficient nonlinear optical materials is crucial for all-optical neural networks.
Existing technologies face limitations in speed and energy efficiency.

Purpose of the Study:

To demonstrate a passive all-chalcogenide all-optical perceptron.
To utilize the nonlinear response of Ge2Sb2Te5 for a nonlinear activation function (NLAF).
To achieve high-speed, energy-efficient all-optical neural classifications.

Main Methods:

Measured sub-picosecond time-resolved optical constants of Ge2Sb2Te5 at 1500 nm.
Designed a Ge2Sb2Te5-tuned microring resonator for the all-optical NLAF.
Employed AlN as the waveguide material for efficient heat dissipation.
Used Sb2S3-tuned Mach Zehnder interferometers (MZIs) for nonvolatile weights.
Tested the scheme with a three-layer deep neural network model.

Main Results:

Achieved a sigmoidal NLAF response with a dynamic range of -9.7 dB.
Determined the NLAF operating speed to be in the nanosecond range via two-temperature analysis.
Obtained a maximum training accuracy of 94.5% for the deep neural network.
Demonstrated energy-efficient all-optical neural classifications at rates > 1 GHz.

Conclusions:

The developed all-optical perceptron scheme is feasible for high-performance computing.
Combining Sb2S3-programmed MZI weights with Ge2Sb2Te5's nonlinear response enables efficient optical neural classifications.
This approach paves the way for next-generation optical neural networks.