Silicon photonic convolution operator exploiting on-chip nonlinear activation function

Nonlinear activation functions (NAFs) are essential in artificial neural networks, enhancing learning capabilities by capturing complex input–output relationships. However, most NAF implementations rely on additional optoelectronic devices or digital computers, reducing the benefits of optical computing. To address this, we propose what we believe to be the first implementation of a nonlinear modulation process using an electro-optic IQ modulator on a silicon photonic convolution operator chip as a novel NAF. We validated this operator by constructing a convolutional neural network for radio machine learning classification, achieving 92.5% accuracy—an improvement of 27% over the case without a NAF. Compared with optoelectronic systems that rely on separate components, this fully integrated silicon photonic chip allows the NAF to execute nearly synchronously with the convolution operation, significantly lowering latency and reducing the complexity of the peripheral control system. This work paves the way for a large-scale on-chip optical neural network computation.