Binary Amplitude Modulation Suppresses Noise Up-Conversion in Coherent Diffractive Optical Networks

Abstract

We establish a fundamental principle in coherent wave-optical computing: restricting the modulation manifold from continuous complex-valued to binary amplitude suppresses stochastic-noise up-conversion while preserving classification fidelity, yielding a counter-intuitive less-is-more robustness law. Seven-layer binary-amplitude-mask D2NN (BM-D2NN) achieve 90.9% (MNIST) and 81.9% (Fashion-MNIST) test accuracy, within 2~4 pp of continuous-modulation D2NN (C-D2NN). Under pixel-wise Gaussian noise N(m,σ^2), spanning zero-mean (shot noise) to nonzero-mean (thermal/readout) regimes, BM-D2NN outperform C-D2NN by up to 32.8 pp (MNIST) and 18.5 pp (Fashion-MNIST). We analytically derive a noise-contribution metric C, governed by a transmission-bias factor K computable from clean data alone, that is consistently smaller for binary modulation than for continuous modulation (as verified for all test samples), guaranteeing the robustness ordering without noisy simulation. BM-D2NN additionally deliver a 6.79-fold higher imaging-plane intensity for clean data input. These results establish a quantitative physical principle connecting modulation-manifold geometry to noise robustness, applicable to any coherent optical processor in the z/λ >> 1 regime.