KIP publications

Artificial intelligence is increasingly deployed in time-critical systems that must convert information into action on sub-microsecond timescales. Integrated photonics offers a route to such low-latency computation, but scalable photonic neural networks remain limited by the lack of compact nonlinear elements. Existing approaches to photonic nonlinearities often rely on optical–electrical–optical conversion that introduces latency overhead, while faster receiverless nonlinear units have primarily been explored in multilayer perceptron architectures requiring large numbers of elements. Here, we experimentally demonstrate a fully CMOS-compatible silicon photonic nonlinear unit based on a photodiode–microring modulator and use it to construct a hardware-grounded model of photonic Kolmogorov–Arnold networks. The programmable photonic nonlinear transfer functions exhibit nanosecond-scale dynamics governed by carrier recombination, with a response time of approximately 8 ns. From static and dynamic measurements, we derive a differentiable physical model and evaluate photonic Kolmogorov–Arnold networks under realistic hardware constraints, including limited photodiode headroom, merge-only routing and finite on-chip integration density. We find that these photonic networks can accurately approximate structured multidimensional functions using compact architectures comprising only a few hundred nonlinear units. These results establish a hardware-grounded route to photonic Kolmogorov–Arnold networks and identify carrier-injection based nonlinearities as a practical building block for ultrafast optical inference.