Synthetic Nonlinearity in Diffractive Optical Networks via Input‐Adaptive Modulation for Intelligent Vision
This work proposes an adaptive modulation strategy driven by the initial input that synthesizes effective nonlinearity within a standard linear optical framework, and reveals that shallow nonlinear architectures can outperform significantly deeper linear counterparts, demonstrating that computational nonlinearity can effectively substitute physical depth.