An Integrated MaxFit Genetic Algorithm-SPICE Framework for 2-Stage Op-Amp Design Automation

The Electronic Design Automation (EDA) tools have achieved high degree of maturity and reliability over the years for digital design. The design of analog circuits is a challenge attributed to the existence of multi-dimensional tradeoffs among multiple analog performance metrics like gain, bandwidth, power dissipation, supply voltage, input/output impedances, linearity, voltage swings and noise. The analog design proves to be a significant bottleneck in a System on Chip (SoC) implementation due to lack of automation techniques. To address this issue, the algorithms of the functioning of human brain or soft computing techniques can be gainfully deployed. This work proposes an integrated MaxFit Genetic Algorithm (GA) and GA-SPICE framework to achieve multi-objective optimization of analog design automation. The design of two-stage op-amp is demonstrated in this framework to optimize the objectives of open-loop DC gain, phase margin, unity gain-bandwidth, slew rate, power dissipation and area. The design is performed by proposed MaxFit GA programming of op-amp design equations in MATLAB environment and the design is transmitted seamlessly to LTspice to perform SPICE simulations for design verification. The dynamic fitness evaluation on SPICE generated performance metrics at each iteration of GA programming by transmitting them to MATLAB environment enhances the robustness of analog design significantly.