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

贝叶斯优化算法与基于散射的仿真方法相结合，用于量子级联检测器（QCD）的优化。QCD在中红外和太赫兹范围内运行，并且与量子级联激光器一起适合集成到诸如气体传感器之类的片上应用中。我们的建模方法基于速率方程模型和基尔霍夫电阻网络进行噪声建模，使用费米黄金法则计算的散射率，或者从整体蒙特卡洛传输方法中提取。贝叶斯优化的适当替代模型基于高斯过程回归，该模型可以处理整体蒙特卡洛模拟中固有的目标函数评估上的噪声偏移。这里， \（\ upmu {\ mathrm {m}} \）。为了进行优化，我们选择特定的检测度作为品质因数，该检测度是信噪比的量度。由于高提取效率和低检测器电导之间的权衡对于良好的检测性能很重要，因此我们寻求理想的层组成并更改不同层叠层的厚度。由于对于经济高效的移动式片上传感应用来说有趣的高温要求，因此选择了300 K的仿真温度。我们的优化策略可将比检测率提高\（{\ sim 2-3} \）在室温下使用两个不同的参数集。此外，我们调查了我们的方法对制造公差的敏感性，显示了优化设计在制造条件下抵抗生长波动的鲁棒性。