AMS Circuit Design Optimization Technique Based on ANN Regression Model With VAE Structure

The advanced design of an analog mixed-signal circuit is not simple enough to meet the requirements of the performance matrix as well as robust operations under process-voltage-temperature (PVT) changes. Even commercial products demand stringent specifications while maintaining the system's performance. The main objectives of this study are to increase the efficiency of the design optimization process by configuring the design process in multiple regression modeling stages, to characterize our target circuit into a regression model including PVT variations, and to enable a search for co- optimum design points while simultaneously checking performance sensitivity. We used an artificial neural network (ANN) to develop a regression model and divided the ANN modeling process into coarse and fine simulation steps. In addition, we applied a variational autoencoder (VAE) structure to the ANN model to reduce the training error due to an insufficient input sample. According to the proposed algorithm, the AMS circuit designer can quickly search for the co- optimum point, which results in the best performance, while the least sensitive operation as the design process uses a regression model instead of launching heavy SPICE simulations. In this study, a voltage-controlled oscillator (VCO) is selected to prove the proposed algorithm. Under various design conditions (CMOS 180 nm, 65 nm, and 45 nm processes), we proceed with the proposed design flow to obtain the best performance score that can be evaluated by a figure-of-merit (FoM). As a result, the proposed regression model-based design flow achieves twice accurate results in comparison to that of the conventional single-step design flow.