Figures‐of‐merit genetic extraction for InGaAs lasers,SiGe low‐noise amplifiers,and ZnSe/Ge/GaAs HBTs

In this paper, we have successfully developed an intellectual parameter‐extraction methodology on the basis of a genetic algorithm (GA), involving the efficient search‐space separation and local‐minima‐convergence prevention schemes. Via an evolutionary simulation tool complemented with appropriate analytic equations, the enhanced approach has been applied to determine the significant figures‐of‐merit (FoMs), including internal quantum efficiency (η_i) as well as transparency current density (J_tr) of semiconductor lasers, minimum noise figure (NF_min) as well as associated available gain (G_A,assoc) of low‐noise amplifiers (LNAs), and DC as well as AC characteristics of heterojunction bipolar transistors (HBTs). For the first time, demonstrated FoM‐extraction results, which coincide well with the actually measured data, for state‐of‐the‐art InGaAs quantum‐well lasers, advanced SiGe LNAs, and abrupt ZnSe/Ge/GaAs HBTs are simultaneously presented to validate this multi‐parameter analysis and robust optimization. Copyright © 2011 John Wiley & Sons, Ltd.