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.