Wavefront-aberration-tolerant diffractive deep neural networks using volume holographic optical elements.

As the demand for computational performance in artificial intelligence (AI) continues to increase, diffractive deep neural networks (DNNs), which can perform AI computing at the speed of light by repeated optical modulation with diffractive optical elements (DOEs), are attracting attention. DOEs are varied in terms of fabrication methods and materials, and among them, volume holographic optical elements (vHOEs) have unique features such as high selectivity and multiplex recordability for wavelength and angle. However, when those are used for DNNs, they suffer from unknown wavefront aberrations compounded by multiple fabrication errors. Here, we propose a training method to adapt the model to be unknown wavefront aberrations and demonstrate a DNN using vHOEs. As a result, the proposed method improved the classification accuracy by approximately 58 percentage points in the optical experiment, with the model trained to classify handwritten digits. The achievement of this study can be extended to the DNN that enables the independent modulation of multiple wavelengths owing to their wavelength selectivity and wavelength division multiplex recordability. Therefore, it might be promising for various applications that require multiple wavelengths in parallel optical computing, bioimaging, and optical communication.