Sensitivity Analysis and Distortion Decomposition of Mildly Nonlinear Circuits
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Volterra Series (VS) is often used in the analysis of mildly nonlinear circuits. In this approach, nonlinear circuit analysis is converted into the analysis of a series of linear circuits. The main benefit of this approach is that linear circuit analysis is well established and direct frequency domain analysis of a nonlinear circuit becomes possible. Sensitivity analysis is useful in comparing the quality of two designs and the evaluation of gradient, Jacobian or Hessian matrices, in analog Computer Aided Design. This thesis presents, for the first time, the sensitivity analysis of mildly nonlinear circuits in the frequency domain as an extension of the VS approach. To overcome efficiency limitation due to multiple mixing effects, Nonlinear Transfer Matrix (NTM) is introduced. It is the first explicit analytical representation of the complicated multiple mixing effects. The application of NTM in sensitivity analysis is capable of two orders of magnitude speedup. Per-element distortion decomposition determines the contribution towards the total distortion from an individual nonlinearity. It is useful in design optimization, symbolic simplification and nonlinear model reduction. In this thesis, a numerical distortion decomposition technique is introduced which combines the insight of traditional symbolic analysis with the numerical advantages of SPICE like simulators. The use of NTM leads to an efficient implementation. The proposed method greatly extends the size of the circuit and the complexity of the transistor model over what previous approaches could handle. For example, industry standard compact model, such as BSIM3V3  was used for the first time in distortion analysis. The decomposition can be achieved at device, transistor and block level, all with device level accuracy. The theories have been implemented in a computer program and validated on examples. The proposed methods will leverage the performance of present VS based distortion analysis to the next level.