In this paper, we present a new ultra fast detector called Low Gain Avalanche Detector (LGAD) with low internal gain. The LGAD is fabricated with conventional APD technology with a modified doping profile, in the multiplication region, which affects the device performance such as: breakdown, multiplication gain and noise factor. For this reason, a numerical method based on Newton-Raphson calculation is proposed to estimate the electrostatic potential and electric field models of low gain avalanche detectors (LGADs) in order to investigate their performances. These models have been validated by their agreement with TCAD numerical simulation results. The effect of Boron doping profile, with different doses in the multiplication region, on the LGAD electrical performance is studied for various device structures in order to extend the device capability to its limit. In addition, LGAD devices are simulated for different temperature considering the effect of the temperature on the multiplication gain.

In recent years, the design and fabrication ofmulti-gate Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) have attracted more efforts due to their high appropriateness for advanced integration circuits’ applications. In fact, the boost of MOSFET structures is a battle against parasitic phenomena appearing at the nanoscale level. Short channel and quantum confinement effects are among the critical drawbacks that need to be remedied carefully. On the other hand, the hot carrier degradation effect is mainly a reliability concern affecting the device per- formance after long duration of work. In response to the high computational costs related to the development of physi- cal based models for Double Gate (DG) MOSFETs including all these effects, more flexible alternatives have been proposed for the prediction of device performances. Our aim in this chapter is to investigate the efficiency of a new proposed frame- work, built upon Kriging metamodeling and Non-dominated Sorting Genetic Algorithm version II (NSGA II), for the optimal design in terms of OFF-current, threshold voltage and swing factor. The input variables of interest are limited to the geometrical parameters namely the channel length and thickness. Data generated according to computer experiments, based on ATLAS 2-D simulator, are used to identify and adjust Kriging surrogate models. It is emphasized that the obtained models can be used accurately in a multi-objective context to offer several Pareto optimal configurations. Therefore, a wide range of selection possibilities is avail- able to the designer depending on situations under consideration.