Machine learning surrogates of high-fidelity electrical models
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This report summarizes the methods and algorithms that were developed on the Sandia National Laboratory LDRD project entitled "Polynomial Chaos methods in Xyce for Embedded Uncertainty Quantification in Circuit Analysis", which was project 200265 and proposal 2019-0817. As much of our work has been published in other reports and publications, this report gives a brief summary. Those who are interested in the technical details are encouraged to read the full published results and also contact the report authors for the status of follow-on projects.
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This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction and portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.
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Parametric sensitivities of dynamic system responses are very useful in a variety of applications, including circuit optimization and uncertainty quantification. Sensitivity calculation methods fall into two related categories: direct and adjoint methods. Effective implementation of such methods in a production circuit simulator poses a number of technical challenges, including instrumentation of device models. This report documents several years of work developing and implementing di- rect and adjoint sensitivity methods in the Xyce circuit simulator. Much of this work sponsored by the Laboratory Directed Research and Development (LDRD) Program at Sandia National Labora- tories, under project LDRD 14-0788.
This report summarizes the methods and algorithms that were developed on the Sandia National Laboratory LDRD project entitled "Advanced Uncertainty Quantification Methods for Circuit Sim- ulation", which was project # 173331 and proposal # 2016-0845. As much of our work has been published in other reports and publications, this report gives an brief summary. Those who are in- terested in the technical details are encouraged to read the full published results and also contact the report authors for the status of follow-on projects.
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