3D photonic integration for scalable neural network computing

Adria Grabulosa; Johnny Moughames; Xavier Porte; Daniel Brunner

doi:10.1117/12.2613357

9 March 2022 3D photonic integration for scalable neural network computing

Adria Grabulosa, Johnny Moughames, Xavier Porte, Daniel Brunner

Proceedings Volume PC12019, AI and Optical Data Sciences III; PC120190C (2022) https://doi.org/10.1117/12.2613357
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

Scalability is essential for computing, yet classical 2D integration of neural networks faces fundamental challenges in this regard. Using 3D printing via two photon polymerization-based direct laser writing, we overcome this challenge and create low loss waveguides and demonstrate dense as well as convolutional network topologies that scale linear in size. Air-clad high-confinement waveguides allow for high-density multimode photonic integration. Leveraging the writing laser’s power as a degree of freedom in a (3+1)D printing technique, we also achieve precise control over refractive index contrast, which enables single mode propagation and low-loss evanescent couplers for next generation 3D integrated photonic circuits.

Conference Presentation

Citation Download Citation

Adria Grabulosa, Johnny Moughames, Xavier Porte, and Daniel Brunner "3D photonic integration for scalable neural network computing", Proc. SPIE PC12019, AI and Optical Data Sciences III, PC120190C (9 March 2022); https://doi.org/10.1117/12.2613357

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