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Time-synthetic optical neural networks with stable programmable gain.

Bei Wu^1,2,3,4, Yudong Ren^1,2,3,4, Rui Zhao^1,2,3,4

¹State Key Laboratory of Extreme Photonics and Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China.

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Optical neural networks (ONNs) achieve faster, more efficient AI by using programmable gain in time-synthetic networks. This approach overcomes stability issues, enabling deeper and more powerful photonic intelligence.

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Area of Science:

Photonics
Artificial Intelligence
Optical Computing

Background:

Optical neural networks (ONNs) promise high speed and energy efficiency for AI.
Passive optical components limit ONN depth due to signal loss and noise accumulation.
Integrating optical gain is challenging due to instability from feedback and reflections.

Purpose of the Study:

To overcome the depth limitations of passive optical neural networks.
To develop a stable method for incorporating optical gain into ONNs.
To enable deeper and more powerful photonic intelligence architectures.

Main Methods:

Developed a time-synthetic optical neural network architecture.
Integrated programmable optical gain into the temporal evolution of the network.
Utilized a causal topology to suppress feedback and parasitic reflections.
Conducted numerical simulations and in-situ experiments for validation.

Main Results:

Achieved stable loss compensation by integrating programmable gain.
Significantly extended the usable depth of the optical neural network.
Demonstrated robust performance on image classification tasks.
Validated the stability and scalability of the gain-assisted time-synthetic ONN approach.

Conclusions:

Gain-assisted time-synthetic ONNs provide a stable and programmable pathway for deep photonic intelligence.
This approach overcomes fundamental limitations of passive optical neural network architectures.
The developed method enables scalable and robust optical artificial intelligence beyond current constraints.