[1] J. W. Goodman, A. R. Dias, L. M. Woody. Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms. Opt. Lett., 2, 1-3(1978).

[2] H. Kwon et al. Nonlocal metasurfaces for optical signal processing. Phys. Rev. Lett., 121, 173004(2018).

[3] R. Hamerly et al. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X, 9, 021032(2019).

[4] A. Silva et al. Performing mathematical operations with metamaterials. Science, 343, 160-163(2014).

[5] B. J. Shastri et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics, 15, 102-114(2021).

[6] G. Wetzstein et al. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2020).

[7] X. Xu et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature, 589, 44-51(2021).

[8] S. M. Kamali et al. Angle-multiplexed metasurfaces: encoding independent wavefronts in a single metasurface under different illumination angles. Phys. Rev. X, 7, 041056(2017).

[9] X. Lin et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

[10] O. Kulce et al. All-optical synthesis of an arbitrary linear transformation using diffractive surfaces. Light Sci. Appl., 10, 196(2021).

[11] M. S. S. Rahman et al. Ensemble learning of diffractive optical networks. Light Sci. Appl., 10, 14(2021).

[12] J. Li et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv. Photonics, 1, 046001(2019).

[13] M. S. S. Rahman, A. Ozcan. Time-lapse image classification using a diffractive neural network(2022).

[14] B. Bai et al. To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects. eLight, 2, 14(2022).

[15] B. Bai et al. Data-class-specific all-optical transformations and encryption. Adv. Mater., 35, 2212091(2023).

[16] Y. Gao et al. Multiple-image encryption and hiding with an optical diffractive neural network. Opt. Commun., 463, 125476(2020).

[17] Y. Su et al. Optical image conversion and encryption based on structured light illumination and a diffractive neural network. Appl. Opt., 62, 6131-6139(2023).

[18] D. Mengu, A. Ozcan. All-optical phase recovery: diffractive computing for quantitative phase imaging. Adv. Opt. Mater., 10, 2200281(2022).

[19] C.-Y. Shen et al. Multispectral quantitative phase imaging using a diffractive optical network. Adv. Intell. Syst., 5, 2300300(2023).

[20] T. Yan et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett., 123, 023901(2019).

[21] B. Bai et al. Pyramid diffractive optical networks for unidirectional magnification and demagnification(2023).

[22] E. Goi, S. Schoenhardt, M. Gu. Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks. Nat. Commun., 13, 7531(2022).

[23] Z. Huang et al. All-optical signal processing of vortex beams with diffractive deep neural networks. Phys. Rev. Appl., 15, 014037(2021).

[24] X. Luo et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light Sci. Appl., 11, 158(2022).

[25] O. Kulce et al. All-optical information-processing capacity of diffractive surfaces. Light Sci. Appl., 10, 25(2021).

[26] M. S. S. Rahman et al. Universal linear intensity transformations using spatially incoherent diffractive processors. Light Sci. Appl., 12, 195(2023).

[27] B. H. Soffer et al. Programmable real-time incoherent matrix multiplier for optical processing. Appl. Opt., 25, 2295-2305(1986).

[28] W. Swindell. A noncoherent optical analog image processor. Appl. Opt., 9, 2459-2469(1970).

[29] J. W. Goodman, L. M. Woody. Method for performing complex-valued linear operations on complex-valued data using incoherent light. Appl. Opt., 16, 2611-2612(1977).

[30] W. Schneider, W. Fink. Incoherent optical matrix multiplication. Opt. Acta Int. J. Opt., 22, 879-889(1975).

[31] A. R. Dias. Incoherent optical matrix-matrix multiplier(1981).

[32] Y. Li et al. Universal polarization transformations: spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer. Adv. Mater., 35, 2303395(2023).

[33] J. Li et al. Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network. Adv. Photonics, 5, 016003(2023).

[34] D. Mengu et al. Misalignment resilient diffractive optical networks. Nanophotonics, 9, 4207-4219(2020).

[35] D. Mengu, Y. Rivenson, A. Ozcan. Scale-, shift-, and rotation-invariant diffractive optical networks. ACS Photonics, 8, 324-334(2021).

[36] R. A. Horn, C. R. Johnson. Matrix Analysis(2012).

[37] J. W. Goodman. Introduction to Fourier Optics(2005).

[38] T. Kozacki, K. Falaggis. Angular spectrum-based wave-propagation method with compact space bandwidth for large propagation distances. Opt. Lett., 40, 3420-3423(2015).

[39] I. Loshchilov, F. Hutter. Decoupled weight decay regularization(2019).

[40] A. Paszke et al. PyTorch: an imperative style, high-performance deep learning library(2019).

[41] G. Cohen et al. EMNIST: extending MNIST to handwritten letters, 2921-2926(2017).