| Summary: | Compact and low-power CMOS-compatible hardware can be used for on-chip optical neural networks (ONNs), enabling affordable and portable image classification solutions for applications like autonomous vehicles, healthcare, and optical communication. In this work, we propose a novel one-dimensional Optical Convolutional Neural Network (OCNN) architecture that significantly reduces the number of learnable parameters required for an ONN. Our OCNN achieves an impressive accuracy of over 96% as a pattern classifier, utilizing only 90 learnable parameters, leading to a simpler structure compared to existing on-chip ONNs. Additionally, our OCNN demonstrates scalability and robustness, with an accuracy exceeding 89% in handwritten digit classification. The OCNN’s convolutional layer employs a lenslet 4f system for convolving desired kernels on input images, while an on-chip lens facilitates the desired Fourier Transform effortlessly. The subsequent layer consists of a single metaline layer, implementing a fully connected layer. By parallelizing pre-trained OCNNs, an on-chip deep convolutional neural network (CNN) can be realized, where each OCNN functions as a separate kernel within a conventional CNN.
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