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Title: Decoding disorder: Machine learning unlocks multi-wavelength and intensity sensing in a single indium oxysulfide phototransistor
Not AvailableFlexible optoelectronic systems face fundamental challenges, including scalable synthesis of uniform, high-performance semiconductors and sensitivity to defects from large-area fabrication. Low-temperature deposition of perovskites, organics, and compound semiconductors promises tunable absorption but introduces structural disorder and sub-gap states that degrade device metrics. Here, we harness electronic disorder in printed sensors via machine learning to decode the photoresponse of three-terminal indium oxysulfide phototransistors fabricated by vacuum-free liquid metal interfacial synthesis. These devices combine wafer-scale uniformity with broadband visible absorption, achieving responsivities >100 A/W and detectivities approaching 7 × 1013 Jones. A cascaded classifier-regressor trained on gate-dependent photocurrent decodes illumination wavelengths (>95% accuracy) and intensities (10 μW/cm2–10 mW/cm2) from a single voltage sweep—without filters or device arrays. To our knowledge, this is the first phototransistor to extract both color and brightness simultaneously. Exploiting disorder to reveal latent information enables low-cost, single-device sensing and multi-parameter detection in flexible electronics.  more » « less
Award ID(s):
2202501 2219991
PAR ID:
10664311
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Cell Press
Date Published:
Journal Name:
Device
ISSN:
2666-9986
Page Range / eLocation ID:
101023
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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