Customized Imperialist Competitive Algorithm Methodology to Optimize Robust Miller CMOS OTAs

The design and optimization of the analog complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) are intrinsically complicated depend heavily on designer’s experience, associated with very long optimization-cycle times. In addition, in order to radiofrequency (RF) CMOS IC work suitably practice, it is necessary perform robustness analyses (RAs) through Simulation Program Integrated Circuit Emphasis (SPICE) simulations, which result still-higher cycle times therefore represent biggest bottleneck launching new electronic products. this context, manuscript aims present, for first time, use a custom imperialist competitive algorithm (ICA) reduce ICs. study, we implement some Miller operational transconductance amplifiers (OTAs) using computational tool named iMTGSPICE, considering two different bulk manufacturing processes from Taiwan Semiconductor Company (TSMC) (180 nm 65 nodes) evolutionary methodologies artificial intelligence, i.e., ICA genetic (GA). main obtained by shows that, an ICA-customized OTAs, possible up 83% relation those implemented GA-customized algorithm, achieving practically same electrical performance.