A statistical model including parameter matching for analog integrated circuits simulation

This paper describes a statistical model for circuit simulation that predicts variations in circuit behavior. This model includes parameter matching considerations critical to analog integrated circuits (IC's). The model is based on experimental data gathered from standard production bipolar-analog integrated chips. By using multivariate statistical techniques, the model is constructred having the two kinds of sub-models. One sub-model uses the eigenvalues and vectors of the correlation matrix, and then generates the correlation between devices on a chip. The other uses linear regression equations and generates the correlation within a device. The model has been implemented into a circuit simulator and statistical circuit simulations are performed. Measured device parameter variations in analog IC's are well reproduced within the practical execution time by the model. Simulation examples for analog IC's are demonstrated to illustrate the effectiveness of the model.