Publications by authors named "Yubin Zang"
Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations.
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- The paper introduces a new data-driven fiber model that uses a deep neural network with multi-head attention to predict signal evolution in optical fiber telecommunications.
- This model offers faster computation times while maintaining accuracy, outperforming traditional methods like the split-step Fourier method (SSFM).
- It effectively balances prediction accuracy and distance generalization, successfully predicting high bit rate (16-QAM 160Gbps) signals over distances of 0 to 100 km, with or without noise.
An integrated physical diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) substrate. This DONN has compact structure and can realize the function of machine learning with whole-passive fully-optical manners. The DONN structure is designed by the spatial domain electromagnetic propagation model, and the approximate process of the neuron value mapping is optimized well to guarantee the consistence between the pre-trained neuron value and the SOI integration implementation.
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