Optimization of a low noise amplifier with two technology nodes using an interactive evolutionary approach

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. However, the design of Radio Frequency (RF) circuits is difficult, time-consuming and based on designer knowledge and experience. This work proposes an interactive evolutionary approach based on genetic algorithm, implemented in the in-house iMTGSPICE optimization tool, to perform the optimization process of a Low-Power Low Noise Amplifier (LNA) dedicated to Wireless Sensor Networks (WSN), which is robust through the corner and Monte Carlo analyses and implemented in two Bulk CMOS technology nodes: 130 nm and 65 nm. Regarding each technology node, we performed two experimental studies to optimize the LNA. The first one used the conventional non-interactive approach of iMTGSPICE, which was not assisted by a designer during the optimization process. The second one used the interactive approach of iMTGSPICE, which was monitored and assisted by a beginner designer during the optimization process. The obtained results demonstrated that the interactive approach of iMTGSPICE performed the optimization process of the robust LNA from 16 to 94% faster than the non-interactive evolutionary approach. The design regarding the technology node of 130 nm took 341 min for the non-interactive and 20 min for the interactive optimization process, whereas the design in the 65 nm took 537 min for the non-interactive and 454 min for the interactive approach.