name: Ahmad Murad subject: Silicon Nitride Waveguide Platform for True Time DelayOptoelectronic Reservoir Computi name: Yuval Bar Ilan Seminar Title: HyBeam: Hybrid Microphone–Beamforming Array-Agnostic Speech Enhancement for Wearables

name: Ahmad Murad MSc in Electro-Optical Engineering Adisor: Professor Alina Karabchevsky Silicon Nitride Waveguide Platform for True Time DelayOptoelectronic Reservoir Computi abstract: This work presents the design, fabrication, and ongoing integration of chip-based silicon nitride (Si3N4) waveguides for true-time delay (TTD) optoelectronic reservoir computing [1]. True-time delay arises from the physical propagation path rather than resonance effects, providing precise timing control and phase-independent delay in photonic systems. We first conducted numerical simulations using finite-element and finite-difference time-domain methods to optimize the geometry of the waveguide, minimize bending losses, and ensure low propagation loss for long optical paths [2]. Based on the optimized design, the Si3N4 waveguides were fabricated in an Archimedean spiral layout to achieve compact optical delays on a meter scale. The fabricated chip is currently being integrated into an optoelectronic reservoir computing system, where it will serve as a key component for implementing on-chip true-time delay dynamics. The upcoming experimental phase aims to evaluate the delay performance and assess the potential of Si3N4 as a low-loss, CMOS-compatible platform for integrated photonic reservoir computing. name: Yuval Bar Ilan Advisor: Prof. Boaz Rafaely Seminar Title: HyBeam: Hybrid Microphone–Beamforming Array-Agnostic Speech Enhancement for Wearables Abstract: Speech enhancement is a fundamental challenge in signal processing, particularly when robustness is required across diverse acoustic conditions and microphone setups. Deep learning methods have been successful for speech enhancement, but often assume fixed array geometries, limiting their use in mobile, embedded, and wearable devices. Existing array-agnostic approaches typically rely on either raw microphone signals or beamformer outputs, but both have drawbacks under changing geometries. We introduce HyBeam, a hybrid framework that uses raw microphone signals at low frequencies and beamformer signals at higher frequencies, exploiting their complementary strengths while remaining highly array-agnostic. Simulations across diverse rooms and wearable array configurations demonstrate that HyBeam consistently surpasses microphone-only and beamformer-only baselines in PESQ, STOI, and SI-SDR. A bandwise analysis shows that the hybrid approach leverages beamformer directivity at high frequencies and microphone cues at low frequencies, outperforming either method alone across all bands. Degree Program: Master’s Degree (M.Sc.) in Electrical and Computer Engineering, Ben-Gurion University of the Negev