Interactive Evolutionary Approach to Reduce the Optimization Cycle Time of a Low Noise Amplifier

Nowadays, wireless communications at frequencies of gigahertz have an increasing demand due to the ever-increasing number of electronic devices that uses this type of communication. They are implemented by Radio Frequency (RF) circuits. However, the design of RF circuits is difficult, time-consuming and based on designer knowledge and experience. This work proposes an interactive evolutionary approach using the genetic algorithm, which is implemented in the in-house iMTGSPICE optimization tool, to perform the optimization process of a robust (corner and Monte Carlo analyses) Ultra Low-Power Low Noise Amplifier (LNA) dedicated to Wireless Sensor Networks (WSN), which is implemented in a 130 nm Bulk CMOS technology. We performed two experimental studies to optimize the LNA. The first one used the interactive approach of iMTGSPICE, which was monitored and assisted by a beginner designer during the optimization process. The second one used the conventional approach of iMTGSPICE (non-interactive), which was not assisted by a designer during the optimization process. The obtained results demonstrated that the interactive approach of iMTGSPICE performed the optimization process of the robust LNA around 94% faster (in approximately 20 minutes only) than the noninteractive evolutionary approach (in approximately 6 hours).