BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Static and reconfigurable polarization shaping towards high-power DTSTART:20251120T140000Z DTEND:20251120T160000Z DTSTAMP:20260802T000845Z UID:f41b49ff-ddf8-48dd-abd5-c4bdb91464cc SEQUENCE:2 CREATED:20251117T095052Z DESCRIPTION:Vector beams are optical fields with anisotropic polarization profiles that enable tight focusing\, multiplexing\, and sensing applicati ons\, among others. Yet\, most generation mechanisms are either alignment- sensitive and lossy\, when using interferometry and/ or SLMs\, or static w hen fabricated\, when relying on metasurfaces\, limiting reconfigurability and high-power use. In this work\, we first benchmark these limitations e xperimentally: an interferometric SLM setup produced a radially polarized beam but revealed residual ellipticity and strong sensitivity to alignment through Stokes polarimetry\, establishing a baseline for improvement. Nex t\, we designed and fabricated an all-silica metasurface to emit radial or azimuthal vector beams. This material is very appealing for high-power ap plications due to its high damage threshold. However\, analyzing the devic e revealed pattern merging\, and experimental observation showed deviation s from the ideal donut\, highlighting the challenges of phase accumulation intrinsic to low-index silica. Building on these research steps\, we prop ose a reconfigurable vector-beam generator that pairs a single\, disorder- engineered birefringent layer with control of the input'\;s complex am plitude\, learned by a polarization-aware\, differentiable Fourier-optics simulator. The machine learning algorithm optimizes the input complex ampl itude to synthesize arbitrary vectorial targets using the same random medi um\, maximizing the overlap between them and the output. By disorder-engin eering with grid-search techniques and by backpropagation methods\, we obt ained overlaps of 0.98\, 0.97\, and 0.92 for a radial\, lemon\, and star t arget\, respectively. We were then able to create an algorithm that perfor ms multi-target optimization using a single birefringent layer. This metho d has the fundamental advantage of being scalable to high-power. LAST-MODIFIED:20251117T095621Z LOCATION:Sala P3 (Piso 1 do Pavilhão de Matemática) do IST URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/static-and-reconfigurable- polarization-shaping-towards-high-power/ X-ALT-DESC;FMTTYPE=text/html:

Vector beams are op tical fields with anisotropic polarization profiles that enable tight focu sing\, multiplexing\, and sensing applications\, among others. Yet\, most generation mechanisms are either alignment-sensitive and lossy\, when usin g interferometry and/ or SLMs\, or static when fabricated\, when relying o n metasurfaces\, limiting reconfigurability and high-power use.

In this work\, we first benchmark these limitations experimentally: an int erferometric SLM setup produced a radially polarized beam but revealed res idual ellipticity and strong sensitivity to alignment through Stokes polar imetry\, establishing a baseline for improvement. Next\, we designed and f abricated an all-silica metasurface to emit radial or azimuthal vector bea ms. This material is very appealing for high-power applications due to its high damage threshold.


However\, analyzing the device reve aled pattern merging\, and experimental observation showed deviations from the ideal donut\, highlighting the challenges of phase accumulation intri nsic to low-index silica. Building on these research steps\, we propose a reconfigurable vector-beam generator that pairs a single\, disorder-engine ered birefringent layer with control of the input'\;s complex amplitud e\, learned by a polarization-aware\, differentiable Fourier-optics simula tor. The machine learning algorithm optimizes the input complex amplitude to synthesize arbitrary vectorial targets using the same random medium\, m aximizing the overlap between them and the output.

By disorder-e ngineering with grid-search techniques and by backpropagation methods\, we obtained overlaps of 0.98\, 0.97\, and 0.92 for a radial\, lemon\, and st ar target\, respectively. We were then able to create an algorithm that pe rforms multi-target optimization using a single birefringent layer. This m ethod has the fundamental advantage of being scalable to high-power.

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