BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Analog logic gate of structured light based on nonlinear optical n eural networks DTSTART:20251120T120000Z DTEND:20251120T140000Z DTSTAMP:20260725T045359Z UID:fc2e584a-eb25-439b-acce-8f8655b23a2f SEQUENCE:2 CREATED:20251117T094810Z DESCRIPTION:Optical Neural Networks combine the typical architecture of a neural network\, with the advantages of using light to store the informati on and perform the operations. They have multiple applications like image recognition or optical computing. Nonlinearity can be added to these syste ms by having layers of materials with a nonlinear response in their intera ction with light. This project aims to use a nonlinear optical neural netw ork as basis for an all optical decoder\, showing the potential of these s ystems. Moreover\, this work focuses on designing and experimental impleme nting the physical neural network. It starts with a study on the Kerr nonl inearity of selected materials in order to choose the best. Such study req uired performing z-scans and I-scans on the samples. Through these measure s\, the best sample to use in the experiment was found to be a gold bearin g glass. Then\, the focus was to improve a machine learning model to act a s a digital twin to the real system. This model served to test system arch itectures and will be responsible for training the parameters later fed in to the physical neural network. Following this\, a real experimental setup was designed\, assembled and aligned. The latter presented several challe nges that had to be overcome. As a first approach\, a linear model was cho sen for testing. The model was trained in silico and then tested in the re al setup\, and such results are presented. The work here presented provide s a ground base for the experimental implementation of an all optical deco der. LAST-MODIFIED:20251117T094820Z LOCATION:Sala P3 (Piso 1 do Pavilhão de Matemática) do IST URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/analog-logic-gate-of-struc tured-light-based-on-nonlinear-optical-neural-networks/ X-ALT-DESC;FMTTYPE=text/html:

Optical Neural Netw orks combine the typical architecture of a neural network\, with the advan tages of using light to store the information and perform the operations. They have multiple applications like image recognition or optical computin g. Nonlinearity can be added to these systems by having layers of material s with a nonlinear response in their interaction with light. This project aims to use a nonlinear optical neural network as basis for an all optical decoder\, showing the potential of these systems.

Moreover\, th is work focuses on designing and experimental implementing the physical ne ural network. It starts with a study on the Kerr nonlinearity of selected materials in order to choose the best. Such study required performing z-sc ans and I-scans on the samples. Through these measures\, the best sample t o use in the experiment was found to be a gold bearing glass.

Th en\, the focus was to improve a machine learning model to act as a digital twin to the real system. This model served to test system architectures a nd will be responsible for training the parameters later fed into the phys ical neural network. Following this\, a real experimental setup was design ed\, assembled and aligned. The latter presented several challenges that h ad to be overcome. As a first approach\, a linear model was chosen for tes ting. The model was trained in silico and then tested in the real setup\, and such results are presented. The work here presented provides a ground base for the experimental implementation of an all optical decoder.

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