A fully real-valued end-to-end optical neural network for generative model

Optical neural networks (ONNs) hold great promise for low-latency, energy-efficient inference. However, the absence of a fully real-valued end-to-end ONN, in which the inputs, weight matrices, and nonlinear activations are all represented in the real-number domain and can be optically cascaded, remains a key bottleneck. Existing approaches either rely on electrical post-processing of photodetector outputs to extend the number field in the linear layers, which breaks optical cascadability, or employ photodiode–driven micro-ring modulators (MRMs) to implement nonlinearities, constraining subsequent-layer inputs to the nonnegative domain and thereby limiting network expressivity and architectural flexibility. Here, we employ two MRMs biased at different resonance wavelengths to achieve real-valued optical encoding, together with a dual-MRM activation element driven by the differential photocurrent of photodiodes, which provides optically cascadable real-valued nonlinear activation. Combined with a real-valued Mach–Zehnder interferometer mesh for matrix computation, this architecture realizes a fully real-valued end-to-end ONN. We experimentally demonstrate a tanh-like nonlinear activation function and validate it on an iris classification task, achieving an accuracy of 98%. We further model the generator of a generative adversarial network based on this structure, in which the nonlinear activation is based on the experimentally measured nonlinear transfer curve. The generator can use natural optical noise as its input, thereby eliminating electro-optic conversion and digital-to-analog conversion at the input stage. With the above merits, the proposed ONN achieves successful optical-to-optical on-chip image generation, validating the superiority of optical computing.