Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Park, Minok; Grbčić, Luka; Motameni, Parham; Song, Spencer; Singh, Alok; Malagrino, Dante; Elzouka, Mahmoud; Vahabi, Puya H.; Todeschini, Alberto; de Jong, Wibe Albert; Prasher, Ravi; Zorba, Vassilia; Lubner, Sean D.

doi:10.1002/advs.202401951

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Journal Article · Mon Apr 29 00:00:00 EDT 2024 · Advanced Science

DOI:https://doi.org/10.1002/advs.202401951· OSTI ID:2341913

^[1]; ^[1]; Motameni, Parham ^[2]; Song, Spencer ^[2]; Singh, Alok ^[1]; Malagrino, Dante ^[2]; ^[1]; Vahabi, Puya H. ^[2]; Todeschini, Alberto ^[3]; de Jong, Wibe Albert ^[1]; ^[4]; ^[4]; ^[5]

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
University of California, Berkeley, CA (United States)
Lucerne University of Applied Sciences and Arts (Switzerland)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Boston Univ., MA (United States)

This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2341913

Alternate ID(s):: OSTI ID: 2341914; OSTI ID: 2406448

Journal Information:: Advanced Science, Vol. 11, Issue 26; ISSN 2198-3844

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

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