EE Seminar: Inverse Design of Diffractive Metasurfaces Using Diffusion Models
הרישום לסמינר יבוצע באמצעות סריקת הברקוד למודל (יש להיכנס לפני כן למודל, לא באמצעות האפליקציה)- הרישום מתחיל ב- 14:55 ומסתיים ב- 15:10
Registration to the seminar is done by scanning the barcode for the Moodle (Please enter ahead to the Moodle, NOT by application)- Registration starts at 14:55, ends at 15:10
Electrical Engineering Systems Seminar
Speaker: Liav Hen
M.Sc. student under the supervision of Prof. Raja Giryes and Dr. Dan Raviv
Wednesday, 29th October 2025, at 15:00
Room 011, Kitot Building, Faculty of Engineering
Inverse Design of Diffractive Metasurfaces Using Diffusion Models
Abstract
Metasurfaces are ultra-thin optical elements composed of engineered sub-wavelength structures that enable precise control of light. Their inverse design - determining a geometry that yields a desired optical response - is challenging due to the complex, nonlinear relationship between structure and optical properties. This often requires expert tuning, is prone to local minima, and involves significant computational overhead.
In this work, we address these challenges by integrating the generative capabilities of diffusion models into computational design workflows. Using an RCWA simulator, we generate training data consisting of metasurface geometries and their corresponding far-field scattering patterns. We then train a conditional diffusion model to predict meta-atom geometry and height from a target spatial power distribution at a specified wavelength, sampled from a continuous supported band. Once trained, the model can generate metasurfaces with low error, either directly using RCWA-guided posterior sampling or by serving as an initializer for traditional optimization methods. We demonstrate our approach on the design of a spatially uniform intensity splitter and a polarization beam splitter, both produced with low error in under 30 minutes. To support further research in data-driven metasurface design, we publicly release our code and datasets.
Liav Hen is an M.Sc. student, studying problems in computer vision using generative AI.
