Hybrid Nanoprinted Neural Networks

Diffractive neural networks (DNNs) exploit the nature of light propagation in free space and the interaction of a light field with metasurfaces designed with machine learning methods to implement inference passively in the optical domain1. In this way, DNNs can achieve all-optical machine learning tasks such as image classification or decryption2. When nanoprinted with two photon nanolithography (TPN) methods, DNNs can express their full potential achieving a neuron density of >500 million neurons per square centimeter, operative wavelength in the near infrared or visible wavelength regime and on-chip integration with imaging sensors, such as commercial complementary metal-oxide-semiconductor (CMOS) sensors3.