  We propose a machine learning-driven optimisation framework for analog
circuit design in this paper. The primary objective is to determine the device
sizes for the optimal performance of analog circuits for a given set of
specifications. Our methodology entails employing machine learning models and
spice simulations to direct the optimisation algorithm towards achieving the
optimal design for analog circuits. Machine learning based global offline
surrogate models, with the circuit design parameters as the input, are built in
the design space for the analog circuits under study and is used to guide the
optimisation algorithm, resulting in faster convergence and a reduced number of
spice simulations. Multi-layer perceptron and random forest regressors are
employed to predict the required design specifications of the analog circuit.
Since the saturation condition of transistors is vital in the proper working of
analog circuits, multi-layer perceptron classifiers are used to predict the
saturation condition of each transistor in the circuit. The feasibility of the
candidate solutions is verified using machine learning models before invoking
spice simulations. We validate the proposed framework using three circuit
topologies--a bandgap reference, a folded cascode operational amplifier, and a
two-stage operational amplifier. The simulation results show better optimum
values and lower standard deviations for fitness functions after convergence.
Incorporating the machine learning-based predictions proposed in the
optimisation method has resulted in the reduction of spice calls by 56%, 59%,
and 83% when compared with standard approaches in the three test cases
considered in the study.
