Nanoimprinted Infrared Metasurface Multispectral Filters for Neural‐Network‐Assisted Low‐Temperature Sensing

Low‐temperature infrared sensing requires temperature information to be extracted from weak thermal radiation while preserving optical throughput. Nanoimprinted metasurface filters combine multispectral encoding with neural‐network calibration to address this challenge. A single circular‐ring geometry controls passband position, bandwidth, and overlap, with simulated tuning across 6.4–11.2 μm. Deep‐ultraviolet stepper lithography and ultraviolet nanoimprinting transfer reusable master patterns to 1‐inch zinc sulfide filters. Four broad, partially overlapping bands retain peak transmittance near 80% and provide complementary spectral inputs for blackbody measurements from −20°C to 20°C. Neural‐network regression on the processed calibration curves achieves a mean absolute error of 0.160°C, compared with 0.434°C for one unfiltered input, a reduction of approximately 63%. Temperature‐resolved comparisons also identify lower errors at the cold end of the range. By connecting geometric spectral design, replicable fabrication, and computational temperature retrieval, this approach offers a route to compact multispectral thermometers that make better use of weak infrared signals.