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Process, supply voltage, and temperature (PVT) are three important factors which contribute to performance variation of the complementary metal–oxide–semiconductor (CMOS) based analog circuits. In this paper, CMOS based analog circuit design with the PVT variation effects are explored. The effects of the PVT variation on the performance of CMOS based analog circuits are introduced. The optimization of CMOS based analog circuits such as differential amplifier (DA) and two-stage operational amplifier (op amp) circuits with PVT variations with different algorithms such as cockoo search (CS), particle swam optimization (PSO), hybrid CSPSO, and differential evaluation (DE) algorithms is presented. Each algorithm is implemented using the C programming language, interfaced with Ngspice circuit simulator, and tested on the Intel®core™ i5, 2.40 GHz processor with 8 GB internal RAM using the Ubuntu operating system (OS). The result shows PVT variation affects the performance of CMOS circuit.

The circuit design of the CMOS based analog part of a mixed-signal integrated circuit (IC) needs a large fraction of the overall design cycle time. The automatic design of an analog circuit is inevitable, seeing recently development of System-on-Chip (SOC) design. This brings about the need to develop computer aided design (CAD) tools for automatic design of CMOS based analog circuits. In this article, a Cuckoo Search (CS) algorithm is presented for automatic design of a CMOS Miller Operational Transconductance Amplifier (OTA). The source code of the CS algorithm is developed using the C language. The Ngspice circuit simulator has been used as a fitness function creator and evaluator. A script file is written to provide an interface between the CS algorithm and the Ngspice simulator. BSIM3v3 MOSFET models with 0.18 µm and 0.35 µm CMOS technology have been used to simulate this circuit. The simulation results of this work are presented and compared with previous works reported in the literature. The experimental simulation results obtained by the CS algorithm satisfy all desired specifications for this circuit.

Abstract-Most design optimization problems in engineering are in general extremely nonlinear and deal with various design variables under complex restrictions. Traditional mathematical optimization procedure may fail to find the optimum solution to real-world problems. Evolutionary Algorithms (EAs) can serve as an efficient approach for these types of optimization problems. In this paper, Particle Swarm Optimization (PSO), Differential Evolution (DE) and Cuckoo Search (CS) algorithms are used to find the optimal solution for some typical unimodal and multimodal benchmark functions. The source codes of all these algorithms are developed using C language and tested on a core i5, 2.4 GHz processor with 8 GB internal RAM. PSO algorithm has a simplicity of implementation and good convergence speed. In contrast, CS algorithm has good ability to find a global optimum solution. To use the advantages of CS and PSO algorithms, a hybrid algorithm of CS and PSO (CSPSO) is implemented and tested with the same benchmark functions. The experimental simulation results obtained by all these algorithms show that hybrid CSPSO outperforms with PSO, DE and CS algorithms.