2019
DOI: 10.1364/oe.27.014009
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Design of optical neural networks with component imprecisions

Abstract: For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise components. We train two ONNs -one with a more tunable design (GridNet) and one with better fault tolerance (FFTNet) -to classify handwritten digits. When simulated without any imperfections, GridNet yields a better accuracy (∼ 98%) than FFTNet (∼ 95%). However, under a small amount of error in their photonic components, the more fault… Show more

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Cited by 149 publications
(108 citation statements)
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References 36 publications
(41 reference statements)
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“…In addition, there will inevitably be fabrication imperfections of the photonic components (star couplers, waveguides, phase shifters, beamsplitters etc.) which break the correspondence between the trained software model and the hardware implementation [28], [56]. Such additional uncertainty introduced by physical implementation becomes non-negligible especially when scaling to a large number of components, hence it is important to evaluate its effects on the photonic CNN performance.…”
Section: B Non-idealities and Fabrication Imperfectionsmentioning
confidence: 99%
See 1 more Smart Citation
“…In addition, there will inevitably be fabrication imperfections of the photonic components (star couplers, waveguides, phase shifters, beamsplitters etc.) which break the correspondence between the trained software model and the hardware implementation [28], [56]. Such additional uncertainty introduced by physical implementation becomes non-negligible especially when scaling to a large number of components, hence it is important to evaluate its effects on the photonic CNN performance.…”
Section: B Non-idealities and Fabrication Imperfectionsmentioning
confidence: 99%
“…Previous studies have considered phase noise of width up to 0.02 rad, which is justified for low index contrast platforms [28], [56]. However, for high index contrast platforms like silicon-on-insulator, the phase errors resulting from imperfections could be up to 2 orders of magnitude greater [57] and hence we considered much larger phase errors.…”
Section: B Non-idealities and Fabrication Imperfectionsmentioning
confidence: 99%
“…Comparing with the ideal DFT, the greatest deviation in amplitude and phase response occurs at the waveguides furthest from the center. Adopting the fidelity measure as a distance metric [28], gives F = 0.997 for the star coupler described above, where || • || denotes division by the Frobenius norm. Another useful summary metric is the overall transmission…”
Section: A Dft Using Star Couplersmentioning
confidence: 99%
“…In addition, there will inevitably be fabrication imperfections of the photonic components (star couplers, waveguides, phase shifters, beam-splitters etc.) which break the correspondence between the trained software model and the hardware implementation [28], [56]. Such additional uncertainty introduced by physical implementation becomes non-negligible especially when scaling to a large number of components, hence it is important to evaluate its effects on the photonic CNN performance.…”
Section: B Non-idealities and Fabrication Imperfectionsmentioning
confidence: 99%
See 1 more Smart Citation