Bayesian optimization of quantum cascade detectors

Abstract

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 $$\upmu {\mathrm{m}}$$ μ 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 $${\sim 2-3}$$ ∼ 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.

Multi-Domain Modeling of Free-Running Harmonic Frequency Comb Formation in Terahertz Quantum Cascade Lasers

“…To model the light-matter interaction in a THz HFC QCL, our open-source Maxwell-DM tool mbsolve is used [49], [50], [68]. All input parameters required for a complete description of the quantum system are extracted from our in-house open-source monacoQC framework [50], [69], a quantum cascade (QC) device simulation tool [70], [71]. In the following, the monacoQC framework with the base library for QC device specifications and the charge carrier transport simulation results are explained in more detail.…”

“…The monacoQC framework is an open-source tool, consisting of an object-oriented Matlab-based device engineering tool for QC structures and including different modules [70], [71]. A schematic overview is depicted in Fig.…”

Electrical flicker-noise analysis based on trapping and de-trapping model in quantum-cascade detectors

“…[1,2,6] Various design concepts of the active region have been proposed with the aim of improving the responsivity and operation temperature, and bandwidth of the response spectrum. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] Owing to progress in the design of the active region, as well as their low-noise property with bias-free photovoltaic operation, the noise-equivalent power of QC detectors compares favorably with the other uncooled IR detectors, such as mercury-cadmium-telluride detectors. [22,23] In addition, highspeed operation with a response time on the order of picoseconds has been demonstrated in mid-IR QC detectors for the detection of a high-frequency heterodyne beat signal, [24,25] microwave rectification, [26] and the observation of an impulse response of a femto-second optical pulse.…”

Utilizing Data-Driven Optimization to Automate the Parametrization of Kinetic Monte Carlo Models

“…However, the combination of BO and GPs in terms of a strong, data-driven surrogate model is yet relatively unexplored. It has been proven to be powerful in the field of compositional engineering, , for high throughput laboratories, for the optimization of quantum cascade detectors, and in kMC models for structural prediction …”

“…38 However, the combination of BO and GPs in terms of a strong, data-driven surrogate model is yet relatively unexplored. It has been proven to be powerful in the field of compositional engineering, 39,40 for high throughput laboratories, 41 for the optimization of quantum cascade detectors, 42 and in kMC models for structural prediction. 43 In this work, we present an innovative data-driven optimization pipeline to enable an automated parametrization of kinetic Monte Carlo models.…”