Bayesian optimization of quantum cascade detectors
نویسندگان
چکیده
Abstract A Bayesian optimization algorithm in combination with a scattering based simulation approach is used for the of quantum cascade detectors (QCDs). QCDs operate mid-infrared and terahertz regime are, together lasers, appropriate integration into on-chip applications such as gas sensors. Our modeling on rate equation model Kirchhoff resistance network noise modeling, using rates calculated Fermi’s golden rule, or alternatively extracted from an ensemble Monte Carlo transport approach. The surrogate Gaussian process regression, which can handle noisy offsets objective function evaluations inherent simulations. Here, we focus matured QCD design detecting at 4.7 $$\upmu {\mathrm{m}}$$ μ m . For choose figure merit specific detectivity, measure signal-to-noise ratio. As trade-off between high extraction efficiency low detector conductance important good detection performance, search perfect layer composition vary thicknesses different layers. Due to high-temperature requirements interesting cost-effective mobile sensing applications, temperature 300 K selected. strategy yields improvement detectivity by factor $${\sim 2-3}$$ ∼ 2 - 3 room two parameter sets. Furthermore, investigate sensitivity our fabrication tolerances, showing robustness optimized designs against growth fluctuations under conditions.
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ژورنال
عنوان ژورنال: Optical and Quantum Electronics
سال: 2021
ISSN: ['1572-817X', '0306-8919']
DOI: https://doi.org/10.1007/s11082-021-02885-0