The performance of long-wave infrared (LWIR) �� = 0.22 HgCdTe avalanche photodiodes (APDs) was presented. The dark currentvoltage characteristics at temperatures 200 K, 230 K, and 300 K were measured and numerically simulated. Theoretical modeling was performed by the numerical Apsys platform (Crosslight). The effects of the tunneling currents and impact ionization in HgCdTe APDs were calculated. Dark currents exhibit peculiar features which were observed experimentally. The proper agreement between the theoretical and experimental characteristics allowed the determination that the material parameters of the absorber were reached. The effect of the multiplication layer profile on the detector characteristics was observed but was found to be insignificant.

Optimization of industrial processes such as manufacturing or processing of specific materials constitutes a point of interest for many researchers, and its application can lead not only to speeding up the processes in question, but also to reducing the energy cost incurred during them. This article presents a novel approach to optimizing the spindle motion of a computer numeric control (CNC) machine. The proposed solution is to use deep learning with reinforcement to map the performance of the reference points realization optimization (RPRO) algorithm used in the industry. A detailed study was conducted to see how well the proposed method performs the targeted task. In addition, the influence of a number of different factors and hyperparameters of the learning process on the performance of the trained agent was investigated. The proposed solution achieved very good results, not only satisfactorily replicating the performance of the benchmark algorithm, but also speeding up the machining process and providing significantly higher accuracy.