What is it about?
The interferometer is a fundamental building block of optical neural networks, however, its nonlinear response limits the accuracy. We demonstrated linearized interferometers that can provide 2 to 3 more bits of precision. The device can directly replace existing interferometers without adding complexity, and the corresponding optical neural network shows higher training accuracy.
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Why is it important?
The conventional interferometer has a sinusoidal transfer function, which leads to inevitable dynamic phase errors. We introduced a superlinear phase change to compensate for the sublinear sinusoidal function, therefore achieving a much more linear response over its entire range.
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This page is a summary of: Low-phase quantization error Mach–Zehnder interferometers for high-precision optical neural network training, APL Photonics, April 2023, American Institute of Physics,
DOI: 10.1063/5.0146062.
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