Problem
Research questions and friction points this paper is trying to address.
nonlinear optical computing
incoherent light
diffractive systems
function approximation
optical hardware
Large-scale nonlinear optical computing with incoherent light via linear diffractive systems
🤖 AI Summary
This work addresses the challenge of performing large-scale nonlinear optical computing under incoherent illumination, where conventional hardware is hindered by weak material nonlinearities and high power requirements. The authors propose a passive, multilayer diffractive processor that relies solely on intensity-encoded inputs and an optimized design to achieve efficient nonlinear function approximation under incoherent or partially coherent light. They demonstrate for the first time that a linear diffractive system can serve as a universal nonlinear approximator, enabling parallel evaluation of millions of functions in a single forward pass through spatial multiplexing at the output. By jointly optimizing hardware parameters using a dense detector array and a model-free, in situ learning strategy, the approach is experimentally validated with an LCD light source, confirming its feasibility and robustness.
📝 Abstract
Nonlinear computation is essential for various information processing tasks. Optical implementations are attractive because passive light propagation can manipulate high-dimensional signals with extreme throughput and parallelism; yet realizing nonlinear mappings in optical hardware remains challenging due to the weak nonlinearity of optical materials and the large intensities required to induce nonlinear interactions. This challenge is further amplified in many systems that operate with incoherent illumination, motivating a coherence-aware framework for scalable optical nonlinear processing. Here, we show that linear optical systems, in particular, optimized diffractive processors comprising passive surfaces, can perform large-scale nonlinear function approximation under spatially incoherent or partially coherent illumination, when preceded by intensity-only input encoding. We quantify how the accuracy of the nonlinear function approximation varies with the degree of parallelism, the number of diffractive layers, and the number of trainable diffractive features. Numerical results demonstrate snapshot computation of up to one million distinct nonlinear functions in a single forward pass through a diffractive processor, with the function outputs spatially multiplexed and read out using densely packed detectors at the output. We further provide a proof-of-concept experimental demonstration under incoherent illumination from a liquid crystal display (LCD), enabled by a model-free in situ learning strategy that jointly optimizes the diffractive profile and detector readout geometry in the presence of hardware imperfections and misalignments. Our findings establish diffractive processors as a massively parallel universal function approximator for both spatially incoherent and partially coherent illumination.
Innovation
Methods, ideas, or system contributions that make the work stand out.
diffractive optical computing
incoherent illumination
nonlinear function approximation
intensity-only encoding
massively parallel optical processing
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