Wavelength‐Multiplexed 2D Beam Steering via a Passive Diffractive Network
ABSTRACT We introduce a wavelength‐addressable diffractive optical network that transforms illumination wavelength into a high‐dimensional control parameter for arbitrarily programmable 2D beam steering. This passive architecture comprises cascaded spatially optimized diffractive layers, jointly designed using deep learning, to rapidly map distinct wavelengths to predefined/desired output angles. Unlike conventional single‐layer dispersive optical elements, which are physically restricted to 1D linear mapping, this framework harnesses complex wavefront transformations to utilize the illumination wavelength as an intrinsic addressing key for arbitrary 2D beam steering, eliminating the need for mechanical scanning or electronic phase control. We numerically demonstrate wavelength‐controlled beam steering across 625 wavelength channels spanning 400–750 nm, realizing a 25 × 25 array of independently addressable beam positions with subwavelength positioning accuracy and high channel fidelity. We further validate the proposed framework experimentally in both the terahertz and visible spectral regimes, demonstrating wavelength‐multiplexed beam steering using 3D fabricated passive diffractive layers at terahertz frequencies and phase‐only spatial light modulators in the visible spectrum. This wavelength‐addressable diffractive architecture establishes a compact and scalable paradigm for high‐speed programmable beam steering, with potential applications in optical communications, routing, imaging, sensing, and emerging photonic information‐processing systems.
Authors
- Mona Jarrahi (ORCID: https://orcid.org/0000-0001-9514-555X)
- Che‐Yung Shen (ORCID: https://orcid.org/0009-0003-0546-6344)
- Çağatay Işıl (ORCID: https://orcid.org/0000-0003-3367-1858)
- Tianyi Gan
- Aydogan Ozcan
- Yuhang Li
Institutions
- California NanoSystems Institute (US)
- University of California, Los Angeles (US)
Publication Details
- Journal
- Advanced Optical Materials
- Published
- 2026-09-14
- DOI
- https://doi.org/10.1002/adom.71782
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00