Additive 3D photonic integration that is CMOS compatible.

3D photonic integration additive manufacturing photonic neural networks

Journal

Nanotechnology
ISSN: 1361-6528
Titre abrégé: Nanotechnology
Pays: England
ID NLM: 101241272

Informations de publication

Date de publication:
24 May 2023
Historique:
received: 26 12 2022
accepted: 27 04 2023
medline: 28 4 2023
pubmed: 28 4 2023
entrez: 27 4 2023
Statut: epublish

Résumé

Today, continued miniaturization in electronic integrated circuits (ICs) appears to have reached its fundamental limit at ∼2 nm feature-sizes, from originally ∼1 cm. At the same time, energy consumption due to communication becomes the dominant limitation in high performance electronic ICs for computing, and modern computing concepts such neural networks further amplify the challenge. Communication based on co-integrated photonic circuits is a promising strategy to address the second. As feature size has leveled out, adding a third dimension to the predominantly two-dimensional ICs appears a promising future strategy for further IC architecture improvement. Crucial for efficient electronic-photonic co-integration is complementary metal-oxide-semiconductor (CMOS) compatibility of the associated photonic integration fabrication process. Here, we review our latest results obtained in the FEMTO-ST RENATECH facilities on using additive photo-induced polymerization of a standard photo-resin for truly three-dimensional (3D) photonic integration according to these principles. Based on one- and two-photon polymerization (TPP) and combined with direct-laser writing, we 3D-printed air- and polymer-cladded photonic waveguides. An important application of such circuits are the interconnects of optical neural networks, where 3D integration enables scalability in terms of network size versus its geometric dimensions. In particular via

Identifiants

pubmed: 37105145
doi: 10.1088/1361-6528/acd0b5
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Creative Commons Attribution license.

Auteurs

Adrià Grabulosa (A)

Institut FEMTO-ST, Université Franche-Comté, CNRS UMR6174, Besançon, France.

Johnny Moughames (J)

Institut FEMTO-ST, Université Franche-Comté, CNRS UMR6174, Besançon, France.

Xavier Porte (X)

Institut FEMTO-ST, Université Franche-Comté, CNRS UMR6174, Besançon, France.

Muamer Kadic (M)

Institut FEMTO-ST, Université Franche-Comté, CNRS UMR6174, Besançon, France.

Daniel Brunner (D)

Institut FEMTO-ST, Université Franche-Comté, CNRS UMR6174, Besançon, France.

Classifications MeSH