PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Adria Grabulosa, Johnny Moughames, Xavier Porte, 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