Implementation of fully connected layers in optical convolutional neural networks for pre-sensor computing

Fully connected computing is a crucial computational function in neural networks. However, in the implementation of optical neural networks, there exist issues such as the absence of fully connected layers under incoherent light conditions and high overhead caused by reliance on electronic layers. To address these problems, this paper proposes an incoherent optical fully connected computing method based on light intensity modulation. A fully connected computing system is constructed using a microlens array (MLA), an amplitude-type liquid crystal spatial light modulator (SLM), and an image sensor. By utilizing the MLA to replicate the target scene into a sub-image array, combined with pixel-wise weight modulation via the SLM and data acquisition by the image sensor, a multi-input multi-output fully connected computing architecture is realized. The design principles for system parameters are derived theoretically, and experiments verify the designed fully connected computing function. A 6-bit linear modulation is achieved through SLM calibration. Finally, an optical neural network system is built, and a classification accuracy of 93.5% on the MNIST dataset is achieved. As an important component of optical neural networks research, this system will be applied to the implementation of all-optical neural networks and holds significant application prospects in the era of artificial intelligence.