Data class-specific all-optical transformations using diffractive computing
Bijie Bai, Heming Wei, Xilin Yang, Tianyi Gan, Deniz Mengü, Mona Jarrahi, Aydogan Özcan · 2023
We present data class-specific transformation diffractive networks that all-optically perform different preassigned transformations for different input data classes. The visual information encoded in the amplitude, phase, or intensity channel of the input field is all-optically processed and transformed/encrypted by the diffractive network. The amplitude or intensity of the resulting field approximates the transformed/encrypted input information using the transformation matrix specifically assigned for that data class. We experimentally validated this class-specific transformation framework by designing and fabricating two diffractive networks at 1550nm and 0.75mm wavelengths. The presented framework provides a fast, secure, and energy-efficient solution to data encryption applications.