Optical micro-phase-shift dropvolume in a diffractive deep neural network.
Opt Lett
; 48(12): 3303-3306, 2023 Jun 15.
Article
em En
| MEDLINE
| ID: mdl-37319087
ABSTRACT
To provide a desirable number of parallel subnetworks as required to reach a robust inference in an active modulation diffractive deep neural network, a random micro-phase-shift dropvolume that involves five-layer statistically independent dropconnect arrays is monolithically embedded into the unitary backpropagation, which does not require any mathematical derivations with respect to the multilayer arbitrary phase-only modulation masks, even maintaining the nonlinear nested characteristic of neural networks, and generating an opportunity to realize a structured-phase encoding within the dropvolume. Further, a drop-block strategy is introduced into the structured-phase patterns designed to flexibly configure a credible macro-micro phase dropvolume allowing for convergence. Concretely, macro-phase dropconnects concerning fringe griddles that encapsulate sparse micro-phase are implemented. We numerically validate that macro-micro phase encoding is a good plan to the types of encoding within a dropvolume.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Opt Lett
Ano de publicação:
2023
Tipo de documento:
Article