Dinc, Niyazi UlasSaba, AmirhosseinMadrid-Wolff, JorgeGigli, CarloBoniface, AntoineMoser, ChristophePsaltis, Demetri2023-01-302023-01-302023-01-302023-01-0410.1515/nanoph-2022-0512https://infoscience.epfl.ch/handle/20.500.14299/194359WOS:000909798800001The prospect of massive parallelism of optics enabling fast and low energy cost operations is attracting interest for novel photonic circuits where 3-dimensional (3D) implementations have a high potential for scalability. Since the technology for data input-output channels is 2-dimensional (2D), there is an unavoidable need to take 2D-nD transformations into account. Similarly, the 3D-2D and its reverse transformations are also tackled in a variety of fields such as optical tomography, additive manufacturing, and 3D optical memories. Here, we review how these 3D-2D transformations are tackled using iterative techniques and neural networks. This high-level comparison across different, yet related fields could yield a useful perspective for 3D optical design.Nanoscience & NanotechnologyMaterials Science, MultidisciplinaryOpticsPhysics, AppliedScience & Technology - Other TopicsMaterials SciencePhysics3d optical memoryadditive manufacturinginverse designoptical tomographyphotonic circuit designdiffraction tomographyneural-networksdata-storageintensityframeworkopticscolortext::journal::journal article::review article