Design of an operational transconductance amplifier configuration using machine learning

Operational transconductance amplifiers (OTA) are critical components in the design of analog circuits. Traditionally, designers have relied on methods such as square law, iterative simulation, and bias point simulation to determine the geometry of amplifiers. However, the miniaturization of transistors introduces effects like short channel and moderate weak inversion that compromise the effectiveness of these techniques. The gm/Id technique adeptly addresses these issues by employing a graphical strategy that analyses transistor characteristics, providing robust solutions to these miniaturization challenges. Building on this, the work used employed machine learning (ML) to further enhance design efficiency and accuracy. By integrating linear and polynomial regression, a significant improvement in the OTA design is demonstrated. The amplifier designed by the regression models yielded a gain of 42 dB for the polynomial regression and 36 dB for the linear regression, as compared to 32 dB in the gm/Id methodology. However, this performance gain incurs a slight power consumption increase of 20 μW. The ML approach demonstrated inherent adaptability and efficiency, particularly in its flexibility and speed when faced with changing input specifications. Polynomial regression model achieved an R2 Score of 0.935 with mean absolute percentage error of 7.5%, indicating high predictive accuracy. This integration of ML significantly reduces design automation time, addressing a major limitation of conventional methods and suggesting a transformative shift in analog circuit design strategies.