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Research demonstrates advancements in thermal imaging with convolutional neural networks, suggesting new approaches for in-sensor computing and machine vision.
In‐sensor computing and logics are the core for advancing machine vision technology, but face challenges in extending such capability into thermal form. Here, a bioinspired thermoreceptor array based on twisted graphene and titanium diselenide (TiSe 2 ) heterostructures is presented. The electron‐phonon interaction between graphene and few‐layer TiSe 2 exhibits strong twist‐angle ( θ ) dependence, as evidenced by varied phonon‐induced gap of graphene (114 ± 13 meV for θ = 1° and 67 ± 6 meV for θ = 7°), enabling sensitive thermal reception at temperature range from 20 to 350 K. Integrating such heterostructure into a (64 × 64) thermoperception array with a convolutional neural network (CNN) framework promotes significant advancements in microscopic thermal imaging, enhancing thermal image detection accuracy by 46% and achieving 99% classification accuracy for pathological cell identification. In addition, the “AND” “OR”, and,“AND/OR” logic operations are demonstrated using these thermoreceptor arrays. This proof‐of‐concept thermoperception arrays also simplify the monolithically integrable architecture of machine vision and offer a potential solution for efficient in‐sensor computing and logics for next‐generation intelligent systems.
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Wang et al. (2025) studied this question.
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