Métodos eficientes na estimativa de produtividade para o dimensionamento automático de circuitos integrados analógicos

Abstract

The analog integrated circuit design within the microelectronics industry has its evolution dictated by the great need for integration of mixed circuits. This trend makes the semiconductor devices are increasingly miniaturized, which implies the increasing complexity in the manufacturing process, resulting in a large variability op parameters. This complexity is directly linked to the design of devices that compose the circuit, since the design space is highly nonlinear. The design of analog circuit can be modeled as an optimization problem and solved by optimization heuristics. The resulting solution is dependent on modeling strategy and performance estimation, which is done generally by electrical simulation. In this context, the UCAF tool was developed. However, the optimized solution falls on the border of the design space where a small variation in device parameters affect circuit performance. This leads to the inclusion of Monte Carlo simulation on the circuit optimization, increasing the computational effort. The main objective of this study is to analyze two different methods of sampling, in order to reduce the number of Monte Carlo runs, and the inclusion of a new heuristic optimization, in order to minimize the time required for the design of the circuit. The Latin hypercube sampling, which requires a smaller number of samples for a reasonable confidence level is used in the first iteration of the optimization process. After a certain point, the sampling method is changed to the traditional random sampling. Heuristic Particle Swarm Optimization was implemented in UCAF tool, due to its low computational cost. The methodology is applied for the design of a Miller and a Telescopic operational transconductance amplifier, showing advantages in terms of processing time and circuit performance. We can demonstrate that the use of a new heuristic, and different methods of sampling for Monte Carlo simulation in the optimization process produces a more efficient search of the design space, and advantages in relation to computational effort.