Femtojoule optical nonlinearity for deep learning with incoherent illumination.

Qixin Feng, Can B Uzundal, Ruihan Guo, Collin Sanborn, Ruishi Qi, Jingxu Xie, Jianing Zhang, Junqiao Wu, Feng Wang

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Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Qixin FengDepartment of Physics, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0000-0003-1756-0090

Can B UzundalMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

Ruihan GuoMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0002-9724-396X

Collin SanbornMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0009-0008-5695-6091

Ruishi QiDepartment of Physics, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0009-0000-1305-1104

Jingxu XieDepartment of Physics, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0009-0000-9691-2474

Jianing ZhangDepartment of Physics, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0009-0004-8090-9536

Junqiao WuMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.ORCID 0000-0002-1498-0148

Feng WangDepartment of Physics, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0000-0001-8369-6194

Funding

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6 · The paper itself

Abstract

Optical neural networks (ONNs) are a promising computational alternative for deep learning due to their inherent massive parallelism for linear operations. However, the development of energy-efficient and highly parallel optical nonlinearities, a critical component in ONNs, remains an outstanding challenge. Here, we introduce a nonlinear optical microdevice array (NOMA) compatible with incoherent illumination by integrating the liquid crystal cell with silicon photodiodes at the single-pixel level. We fabricate NOMA with more than half a million pixels, each functioning as an optical analog of the rectified linear unit at ultralow switching energy down to 100 femtojoules per pixel. With NOMA, we demonstrate an optical multilayer neural network. Our work holds promise for large-scale and low-power deep ONNs, computer vision, and real-time optical image processing.

Identifiers

PMID39888986

DOI10.1126/sciadv.ads4224

PMCPMC11784804

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