Machine Learning Based Analog Circuit Design Automation

This work presents the automated design for a common-source (CS) amplifier that includes a diode-connected load using a machine learning-based method [19]. While considering the limitations placed on the circuit by the diode-connected load using the NN model, the aim is to maximize the performance characteristics including gain and power consumption. To get this, a collection of simulated CS amplifiers with diode-connected loads gathers a dataset including different input parameters and corresponding circuit performance measures. Then trained on this dataset, deep learning algorithms—more especially, Multi-layer perceptron models—establish the link between the input parameters with the desired outcome metrics. Later on, automated circuit design makes use of the trained models. The machine learning algorithm forecasts the best values for the circuit parameters—including transistor dimensions, biassing, and load characteristics—given the desired specifications and constraints. Faster and more effective circuit design results from this method’s reduction of the design iteration time and reliance on hand tuning. Extensive simulations & comparisons with varying number of datasets help to assess the efficacy of the suggested approach by means of performance of estimation with regard to dataset. The results show that the automated design based machine learning method produces better RMSE and MSE of aspect ratio estimation. Furthermore, the automated design process shows scalability and dependability over several design criteria. Finally, this work presents a fresh method using machine learning techniques to automate CS amplifier design with diode-connected loads. Regarding performance, adaptability, and efficiency, the suggested approach presents notable benefits. It has great possibility to hasten the evolution of analog circuits and promote circuit design innovations.