A Hybrid GA-SQP Algorithm for Analog Circuits Sizing

This study presents a hybrid algorithm obtained by combining a genetic algorithm (GA) with successive quadratic sequential programming (SQP), namely GA-SQP. GA is the main optimizer, whereas SQP is used to refine the results of GA, further improving the solution quality. The problem formulation is done in the framework named RUNE (fRamework for aUtomated aNalog dEsign), which targets solving nonlinear mono-objective and multi-objective optimization problems for analog circuits design. Two circuits are presented: a transimpedance amplifier (TIA) and an optical driver (Driver), which are both part of an Optical Network-on-Chip (ONoC). Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results obtained with SQP algorithm. The outcome is very encouraging and suggests that the hybrid proposed method is very efficient in solving analog design problems.

Switching Optimization for Class-G Audio Amplifiers with Two Power Supplies

This paper presents a system-level method to decrease the power consumption of integrated audio Class-G amplifiers for mobile phones by using the same implementation of the level detector, but by changing the parameters of the switching algorithm. This method uses an optimization based on a simplified model simulation to quickly find the best power supply switching strategy in order to decrease the losses of the internal Class-AB amplifier. Using a few relevant equations of Class-G on the electrical level and by reducing the number of calculation points, this model can dramatically reduce the calculation time to allow power consumption evaluation in realistic case conditions compared to the currently available tools. This simplified model also evaluates the audio quality reproduction thanks to a psycho-acoustic method. The model has been validated by comparing model results and practical measurements on two industrial circuits. This proposed model is used by an optimizer based on a genetic algorithm associated with a pattern search algorithm to find the best power supply switching strategy for the internal Class-AB amplifier. The optimization results improve life-time performance by saving at least 25% in power consumption for typical use-case (1mW) compared to the industrial circuit studied and without losses in audio quality.