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In this work, we provide important parameters to take into account when fabricating in-plane III-V photodetectors (PDs) integrated on silicon (Si) using template-assisted selective epitaxy (TASE). Their performance has previously been demonstrated, and here, we have investigated the cause of the device-to-device variability in their current-voltage (I–V) characteristics. We do this by fabricating devices with variations of the geometry and analyzing their behavior by comparison with technology computer-aided design (TCAD) simulations. We demonstrate the crucial role of the crystal orientation of the substrate, which may lead to nonuniform growth rate, length, and growth front shape. With the use of TCAD simulations, we have illustrated the relevance of the position of the i-region with respect to the metal contacts of the device.

This work describes a novel simulation approach that combines machine learning (ML) and device modeling simulations. The device simulations are based on the quantum mechanical nonequilibrium Green’s function (NEGF) approach, and the ML method is an extension of a convolutional generative network. We have named our new simulation approach ML-NEGF. It is implemented in our in-house simulator called Nano-Electronics Simulation Software (NESS). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the “standard” NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behavior, resulting in faster convergence of the coupled Poisson-NEGF self-consistency simulations. Quantitatively, our ML-NEGF approach achieves an average convergence speedup of 60%, substantially reducing the computational time while maintaining the same accuracy.

In this work, we presented a novel simulation tool called the Biomolecule-Oxide Simulator (BOxSim) and its application in simulating the behavior of BioFETs. The capabilities of BOxSim are shown by investigating the impact of various high-k dielectric materials on important Figures of Merits such as current in the channel of the device, surface potential, sensitivity, and intrinsic buffer capacitance. The capabilities of our BOxSim framework allow us to discover new features in the variation of differential capacitance with respect to the second gradient of drain current and surface potential. The profile of the surface potential is used to uniquely identify the signatures (fingerprints) of amphoteric molecules like acids or peptides. The proposed model prospects the possibility to develop an efficient method for protein sequencing. The reliability of the designed model is confirmed by calibrating the simulated results with experimental data for different physical conditions. The results presented in this paper demonstrate the potential of this framework for advancing the field of biosensors and bioelectronics for the biorecognition of different molecules.