We are happy to announce the publication of  the Highlights 2022 summarizing the main activities and achievements of the Nanoscience Laboratory in 2022.

We are happy to welcome our new PhD student Salamat Ali. Salamat will work within the project Q@TN.

On the effect of the thermal cross-talk in a photonic feed-forward neural network based on silicon microresonators

S. Biasi, R. Franchi, D. Bazzanella and L. Pavesi

Frontiers in Physics, 23 December 2022 Sec. Optics and Photonics

URL: https://doi.org/10.3389/fphy.2022.1093191

Photonics as a key enabling technology: light and communication

Dr. Mattia Mancinelli, University of Trento; Dr. Alessandro Sturniolo, SM Optics; Dr. Marco Avesani, University of Padova

Abstract

Photonics studies photons and their interaction with matter, both in terms of fundamental and applied research. The aim of this science is to understand the phenomena ruling the behavior of photons, in order to boost the development of novel technologies and applications based on light. In this sense, photonics is a key enabling technology, with its central role in many fields of science such as sensing, telecommunication, quantum physics, machine learning, and biology.  In this workshop, three researchers in photonics will present important applications of optics and photonics in the field of communication: optical fiber for data transmission using light, the impact of photonics in communication networks, and cryptography using Quantum Key Distribution.

Integrated high-neuron-density diffractive neural networks  performing near-infrared inference         

Dr. Elena Goi, University of Shanghai for Science and Technology, Shanghai, China

Abstract

Optical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical  signals such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed  of light. In this work, such optical devices for data processing in the form of multi-layer nanoscale diffractive neural networks  trained to perform optical inference tasks are presented. We show the functionality of these passive optical devices on the  example of decryptors trained to perform optical inference through symmetric and asymmetric decryption and multi-layer  diffractive neural networks for direct phase retrieval. The perceptrons, designed for operation in the near-infrared region,  are nanoprinted on complementary metal-oxide–semiconductor chips by galvo-dithered two-photon nanolithography with  axial nanostepping of 10 nm, achieving a neuron density of 108 neurons / mm3. The compact form factor of the resulting  optical neural networks and the lithographic fabrication technology that allow for directly integration on opto-electronic  sensors, enable the co-integration of the optical perceptrons with additional layers of electronic neural networks, or the  use of the sensor’s nonlinear response as nonlinear activation function, in this way forming deep neural networks. This  power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical  decryption, sensing, microscopy, high-precision laser nanolithography, medical diagnostics, and computing.