Publications

Results 1–25 of 54
Skip to search filters

Advanced Uncertainty Quantification Methods for Circuit Simulation: Final Report LDRD 2016-0845

Keiter, Eric R.; Swiler, Laura P.; Wilcox, Ian Z.

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.

More Details
TYPE SAND Report YEAR 2016
OSTIDOI

Computational Algorithms for Device-Circuit Coupling

Keiter, Eric R.; Keiter, Eric R.; Hutchinson, Scott A.; Hoekstra, Robert J.; Rankin, Eric R.; Russo, Thomas V.; Waters, Lon J.

Circuit simulation tools (e.g., SPICE) have become invaluable in the development and design of electronic circuits. Similarly, device-scale simulation tools (e.g., DaVinci) are commonly used in the design of individual semiconductor components. Some problems, such as single-event upset (SEU), require the fidelity of a mesh-based device simulator but are only meaningful when dynamically coupled with an external circuit. For such problems a mixed-level simulator is desirable, but the two types of simulation generally have different (sometimes conflicting) numerical requirements. To address these considerations, we have investigated variations of the two-level Newton algorithm, which preserves tight coupling between the circuit and the partial differential equations (PDE) device, while optimizing the numerics for both.

More Details
TYPE Report YEAR 2003
OSTIDOI

Development, Demonstration and Validation of Data-Driven Compact Diode Models for Circuit Simulation and Analysis

Aadithya, Karthik V.; Kuberry, Paul A.; Paskaleva, Biliana S.; Bochev, Pavel B.; Leeson, Kenneth M.; Mar, Alan M.; Mei, Ting M.; Keiter, Eric R.

Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (e.g., radiation effects) into an existing model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2) Generalized Moving Least-Squares, and (3) feedforward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these "data-driven" compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.

More Details
TYPE Other Report YEAR 2019
OSTIDOI

Improving performance via mini-applications

Doerfler, Douglas W.; Crozier, Paul C.; Edwards, Harold C.; Williams, Alan B.; Rajan, Mahesh R.; Keiter, Eric R.; Thornquist, Heidi K.

Application performance is determined by a combination of many choices: hardware platform, runtime environment, languages and compilers used, algorithm choice and implementation, and more. In this complicated environment, we find that the use of mini-applications - small self-contained proxies for real applications - is an excellent approach for rapidly exploring the parameter space of all these choices. Furthermore, use of mini-applications enriches the interaction between application, library and computer system developers by providing explicit functioning software and concrete performance results that lead to detailed, focused discussions of design trade-offs, algorithm choices and runtime performance issues. In this paper we discuss a collection of mini-applications and demonstrate how we use them to analyze and improve application performance on new and future computer platforms.

More Details
TYPE SAND Report YEAR 2009
OSTIDOI
Results 1–25 of 54
Results 1–25 of 54