Numerical Optimization of Quantum Cascade Detector Heterostructures

We demonstrate a Bayesian optimization framework for quantum cascade (QC) devices in the mid-infrared (mid-IR) and terahertz (THz) regime. The optimization algorithm is based on Gaussian process regression (GPR) and the devices are evaluated using a perturbed rate equation approach based on scattering rates calculated self-consistently by Fermi's golden rule or alternatively extracted from an Ensemble Monte Carlo (EMC) simulation tool. Here, we focus on the optimization of a mid-IR quantum cascade detector (QCD) at a wavelength of 4.7μm with respect to the specific detectivity as a measure for the signal to noise ratio. At a temperature of 220 K we obtain an improvement in specific detectivity by a factor ~ 2.6 to a value of 2.6×10
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Jones.