Binary Amplitude Modulation Suppresses Noise Up-Conversion in Coherent Diffractive Optical Networks
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918531200712704 |
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| author | Lim, Hyuntae Kim, Kyoungsik |
| author_facet | Lim, Hyuntae Kim, Kyoungsik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30820 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Binary Amplitude Modulation Suppresses Noise Up-Conversion in Coherent Diffractive Optical Networks Lim, Hyuntae Kim, Kyoungsik Optics Applied Physics 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. |
| title | Binary Amplitude Modulation Suppresses Noise Up-Conversion in Coherent Diffractive Optical Networks |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2605.30820 |