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Tilted-Mode All-Optical Diffractive Deep Neural Networks
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Diffractive deep neural networks (D2NNs) typically adopt a densely cascaded arrangement of diffractive masks, leading to multiple reflections of diffracted light between adjacent masks, thereby affecting the network’s inference capability. It is challenging to fully simulate this multiple-reflection phenomenon. To eliminate this phenomenon, we designed tilted-mode all-optical diffractive deep neural networks (T-D2NNs) and proposed a theoretical model for diffraction propagation in the tilted mode. Simulation results indicate that T-D2NNs address the performance degradation caused by interlayer reflections in D2NNs constructed with high-index diffractive masks. In classification tasks, T-D2NNs achieve better classification results compared to D2NNs that consider interlayer reflections.
Title: Tilted-Mode All-Optical Diffractive Deep Neural Networks
Description:
Diffractive deep neural networks (D2NNs) typically adopt a densely cascaded arrangement of diffractive masks, leading to multiple reflections of diffracted light between adjacent masks, thereby affecting the network’s inference capability.
It is challenging to fully simulate this multiple-reflection phenomenon.
To eliminate this phenomenon, we designed tilted-mode all-optical diffractive deep neural networks (T-D2NNs) and proposed a theoretical model for diffraction propagation in the tilted mode.
Simulation results indicate that T-D2NNs address the performance degradation caused by interlayer reflections in D2NNs constructed with high-index diffractive masks.
In classification tasks, T-D2NNs achieve better classification results compared to D2NNs that consider interlayer reflections.
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