This paper details the Particle Swarm Optimization (PSO) technique for the optimal design of analog circuits. It is shown the practical suitability of PSO to solve both mono-objective and multiobjective discrete optimization problems. Two application examples are presented: maximizing the voltage gain of a low noise amplifier for the UMTS standard and computing the Pareto front of a biobjective problem, maximizing the high current cut off frequency and minimizing the parasitic input resistance of a second generation current conveyor. The aptness of PSO to optimize difficult circuit problems, in terms of numbers of parameters and constraints, is shown.

In this paper, we propose an improved translinear based CCII configuration. Heuristic algorithm is used for optimal sizing regarding static and dynamic performances. PSPICE simulations for AMS 0.35 µm CMOS technology show that the current and voltage bandwidths are respectively 2.6 GHz and 3.9 GHz, and the parasitic resistance at port X (R X ) has a value of 18 for a control current of 100 µA. The improved configuration is used as a building block into high frequency design applications: a current controlled oscillator and a tunable fully integrable band pass filter. The oscillator frequency can be tuned in the range of [290-475 MHz] by a simple variation of a DC current. The central frequency of the band pass filter can be varied in the range of [1.22-1.56 GHz] and the quality factor vary in the range [8-306] with a simple variation of a DC current.

Optimally designing switched current (SI) memory cells is a hard task. In addition, it is usually limited to the design of ideal cells. Thus, in this paper we deal with optimizing these cells and precisely real ones using a heuristic. Since SI class AB grounded gate memory cells are well known to be improved cells, we applied the proposed heuristic to design this kind of cells. Also, besides maximizing performances and minimizing famous error sources, we focus on optimally sizing transistors forming switches and bias sources. The optimization procedure, developed with help of C++ software, allows automatic design of the cell. It is also highlighted in the followings.

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