Results for “circuit simulation” — Page 10

DOE OSTI2021

Xyce™ Parallel Electronic Simulator Reference Guide (V.7.4)

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Xyce™ Parallel Electronic Simulator Reference Guide, Version 7.5

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Xyce™ Parallel Electronic Simulator Reference Guide (V.7.6)

This document is a reference guide to the Xyce™ Parallel Electronic Simulator, and is a companion document to the Xyce™ Users' Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce™. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce™ Users' Guide.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2012

Verilog-A Device Models for Cryogenic Temperature Operation of Bulk Silicon CMOS Devices

Verilog-A based cryogenic bulk CMOS (complementary metal oxide semiconductor) compact models are built for state-of-the-art silicon CMOS processes. These models accurately predict device operation at cryogenic temperatures down to 4 K. The models are compatible with commercial circuit simulators. The models extend the standard BSIM4 [Berkeley Short-channel IGFET (insulated-gate field-effect transistor ) Model] type compact models by re-parameterizing existing equations, as well as adding new equations that capture the physics of device operation at cryogenic temperatures. These models will allow circuit designers to create optimized, reliable, and robust circuits operating at cryogenic temperatures.

Akturk, Akin↗

DOE OSTI2013

Xyce parallel electronic simulator reference guide, version 6.0.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide [1] .

Keiter, Eric R↗

DOE OSTI2014

Xyce parallel electronic simulator reference guide, Version 6.0.1.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide [1] .

Keiter, Eric R↗

DOE OSTI2015

Xyce Parallel Electronic Simulator Reference Guide Version 6.4

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce . This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide [1] . Trademarks The information herein is subject to change without notice. Copyright c 2002-2015 Sandia Corporation. All rights reserved. Xyce TM Electronic Simulator and Xyce TM are trademarks of Sandia Corporation. Portions of the Xyce TM code are: Copyright c 2002, The Regents of the University of California. Produced at the Lawrence Livermore National Laboratory. Written by Alan Hindmarsh, Allan Taylor, Radu Serban. UCRL-CODE-2002-59 All rights reserved. Orcad, Orcad Capture, PSpice and Probe are registered trademarks of Cadence Design Systems, Inc. Microsoft, Windows and Windows 7 are registered trademarks of Microsoft Corporation. Medici, DaVinci and Taurus are registered trademarks of Synopsys Corporation. Amtec and TecPlot are trademarks of Amtec Engineering, Inc. Xyce 's expression library is based on that inside Spice 3F5 developed by the EECS Department at the University of California. The EKV3 MOSFET model was developed by the EKV Team of the Electronics Laboratory-TUC of the Technical University of Crete. All other trademarks are property of their respective owners. Contacts Bug Reports (Sandia only) http://joseki.sandia.gov/bugzilla http://charleston.sandia.gov/bugzilla World Wide Web http://xyce.sandia.gov http://charleston.sandia.gov/xyce (Sandia only) Email xyce@sandia.gov (outside Sandia) xyce-sandia@sandia.gov (Sandia only)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Xyce Parallel Electronic Simulator Reference Guide Version 6.6.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce . This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide [1] . The information herein is subject to change without notice. Copyright c 2002-2016 Sandia Corporation. All rights reserved. Acknowledgements The BSIM Group at the University of California, Berkeley developed the BSIM3, BSIM4, BSIM6, BSIM-CMG and BSIM-SOI models. The BSIM3 is Copyright c 1999, Regents of the University of California. The BSIM4 is Copyright c 2006, Regents of the University of California. The BSIM6 is Copyright c 2015, Regents of the University of California. The BSIM-CMG is Copyright c 2012 and 2016, Regents of the University of California. The BSIM-SOI is Copyright c 1990, Regents of the University of California. All rights reserved. The Mextram model has been developed by NXP Semiconductors until 2007, Delft University of Technology from 2007 to 2014, and Auburn University since April 2015. Copyrights c of Mextram are with Delft University of Technology, NXP Semiconductors and Auburn University. The MIT VS Model Research Group developed the MIT Virtual Source (MVS) model. Copyright c 2013 Massachusetts Institute of Technology (MIT). The EKV3 MOSFET model was developed by the EKV Team of the Electronics Laboratory-TUC of the Technical University of Crete. Trademarks Xyce TM Electronic Simulator and Xyce TM are trademarks of Sandia Corporation. Orcad, Orcad Capture, PSpice and Probe are registered trademarks of Cadence Design Systems, Inc. Microsoft, Windows and Windows 7 are registered trademarks of Microsoft Corporation. Medici, DaVinci and Taurus are registered trademarks of Synopsys Corporation. Amtec and TecPlot are trademarks of Amtec Engineering, Inc. All other trademarks are property of their respective owners. Contacts World Wide Web http://xyce.sandia.gov https://info.sandia.gov/xyce (Sandia only) Email xyce@sandia.gov (outside Sandia) xyce-sandia@sandia.gov (Sandia only) Bug Reports (Sandia only) http://joseki-vm.sandia.gov/bugzilla http://morannon.sandia.gov/bugzilla

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Xyce Parallel Electronic Simulator Reference Guide Version 6.7.

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce . This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide [1] . The information herein is subject to change without notice. Copyright c 2002-2017 Sandia Corporation. All rights reserved. Trademarks Xyce TM Electronic Simulator and Xyce TM are trademarks of Sandia Corporation. Orcad, Orcad Capture, PSpice and Probe are registered trademarks of Cadence Design Systems, Inc. Microsoft, Windows and Windows 7 are registered trademarks of Microsoft Corporation. Medici, DaVinci and Taurus are registered trademarks of Synopsys Corporation. Amtec and TecPlot are trademarks of Amtec Engineering, Inc. All other trademarks are property of their respective owners. Contacts World Wide Web http://xyce.sandia.gov https://info.sandia.gov/xyce (Sandia only) Email xyce@sandia.gov (outside Sandia) xyce-sandia@sandia.gov (Sandia only) Bug Reports (Sandia only) http://joseki-vm.sandia.gov/bugzilla http://morannon.sandia.gov/bugzilla

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Xyce Parallel Electronic Simulator Reference Guide Version 6.10

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guid [1] . The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce . This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Xyce Parallel Electronic Simulator Reference Guide (V.6.12)

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide [1]. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Problem-tailored Simulation of Energy Transport on Noisy Quantum Computers

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with L = 12 sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis – the Pauli Y basis – in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We discuss the applicability of the proposed sampling approach beyond the mixed-field Ising chain and formulate a variational method to search for a sampling basis with small sample-to-sample fluctuations for an arbitrary Hamiltonian. This opens the door to applying these techniques in more general models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

A sensing material-free and simple readout MEMS sensor for detecting Helium

Abstract In this work, we report a method that enables a standard electrostatic MEMS device to perform complex sensing functionalities, such as detecting the presence of helium without a sensing material or a conditioning circuit. Helium is a noble, odorless, non-reactive gas that is very challenging to detect. It is used in critical applications such as storing nuclear fuel waste inside a dry cask. In these applications, its leakage from the dry cask may indicate the cask's safe operation's degradation. A departure from the common practice of exciting the MEMS around its mechanical resonance, the method is based on exciting the MEMS around its electrical resonance circuit. This method shows that the tiny difference between the air dielectric constant (1.00059) and helium (1.000067) corresponding to only a few Femtofarad level capacitances produces a 25 mV difference without a conditioning circuit. Simulation results confirmed those findings and explored the sensor response at different operation conditions. This method eliminates the need for a heated microstructure and the need for absorption material. This method is not limited to gas sensing. It can be applied to other sensing mechanisms, such as acceleration and pressure measurements, and eliminate the complex circuit to read small capacitance in these applications.

Mohaidat, Sulaiman↗

DOE OSTI2014

Transport properties of nanocomposite and its simulation with L-R-C circuit

The nano particles are represented in this communication by L-R-C equivalent circuit. The dc current voltage characteristics (CVC) of the proposed circuit have simulated using Circuit-Maker ® 2000. Experimental investigation on ZnO nano-composite with capping material gum acacia shows similar CVC. NPs are represented by C-R combinations to manifest the Coulomb blockade effect of a quantum dot. The capping material is represented by an inductor along with a resistance in series. Nine NPs with capping matrix are simulated. The dc current voltage characteristics (CVC) and gross feature of polarization nature obtained by experiment and simulation study are consistent.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

Electrical power converters are critical to a wide range of applications ranging from renewable integration to transportation electrification, and can be a key factor determining the size, weight, and efficiency of energy conversion systems. Magnetic components are typically the largest and least efficient components in power electronics. While there have been major strides in the modeling and analysis of power semiconductor devices and circuit simulations, the necessary advances in the design of power magnetics have lagged. In this project, we have transformed the modeling and design of power magnetics with machine learning enabled methods and catalyze simultaneous disruptive improvements for ML-based power electronics design tools. A fully automated open-source machine learning based magnetics modeling platform – the MagNet project - with innovations in full stack have been developed to greatly accelerate the design process and provide new insights to magnetic material and geometry design. The ARPA-E funded MagNet platform contains three major building blocks: 1) a ML-Integrated Data Acquisition System (MIDAS): a highly automated data acquisition testbed which is capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations; 2) a ML-integrated Core Loss Model (MICLM): a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials for arbitrary excitation waveforms; 3) ML-guided Magnetics SPICE Simulation Tool (PMSPICE): a fully integrated CAD tool which can simulate the magnetics in SPICE. It can help the designers to quickly model the linear and non-linear characteristics of magnetic components and evaluate their behavior in SPICE simulations. The developed MagNet system has fully demonstrated the proposed performance target and has been open sourced to the entire power electronics community to advance the modeling and design of power magnetics from many different angles.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Preliminary design and optimization of a G-band extended interaction oscillator based on a pseudospark-sourced electron beam

The design and simulation of a G-band extended interaction oscillator (EIO) driven by a pseudospark-sourced electron beam is presented. The characteristic of the EIO and the pseudospark-based electron beam were studied to enhance the performance of the newly proposed device. The beam-wave interaction of the EIO can be optimized by choosing a suitable pseudospark discharging voltage and by widening the operating voltage region of the EIO circuit. Simulation results show that a peak power of over 240 W can be achieved at G-band using a pseudospark discharge voltage of 41 kV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2011

Lockout/Tagout (LOTO) Simulator

The Lockout/Tagout (LOTO) Simulator is a portable training aid, or demonstration tool, designed to physically illustrate real-time critical-safety concepts of electrical lockout/tagout. The objective is to prevent misinterpretations of what is off and what is on during maintenance and repair of complex electrical systems. The simulator is designed in the form of a hinged box that opens up and stands on its own as an easel for demonstrations. On the outer face of the unit is a simulated circuit breaker box housing the switches. The breakers control the main power to the unit, a light bulb, and an electrical control cabinet. The light bulb is wired so that either of two breakers can provide power to it. When power is sent to the electrical control cabinet, a red indicator light illuminates. Inside the cabinet is the power supply from a personal computer. The power supply produces a 12-V dc output that is sent over to a small fan next to it, also from a computer, and an amber light on the front of the cabinet illuminates. A separate switch powers the fan on and off. The power supply is behind a plastic shield to protect against exposure to live conductors. Electrical banana jacks are mounted in the plastic shield to allow a voltmeter to be connected safely when opening the cabinet and taking a meter reading to verify de-energization as part of a simulation exercise. This LOTO simulator prototype is designed and fabricated as an all-in-one unit. All accessories can be stored inside the hinged case, and there is a handle on top for ease of transport. The circuit breaker labels attach with hook and loop fasteners so that they may be moved and changed to fit the training or demonstration scenario. The warning signs and labels on the electrical control box are magnetic, allowing for easy reconfiguration to emulate different equipment setups. A specially designed magnetic cover was made to disguise the indicator lights for demonstrations when these indicators are not used. The cover is disguised as an arc flash safety label that would typically be found on such a cabinet. One indicator light has a separate switch that can take it offline. This is to allow for demonstration to trainees on why it is important not to completely rely on indicator lights, but that they should always take a meter reading at the exposed conductors to absolutely verify de-energization before exposure. A clear plastic barrier and banana jacks inside the cabinet provide a safe way to plug in a voltmeter for demonstrations without exposure to the hazards of energized equipment. A small remote control unit is wired into the fan circuit. The remote allows the demonstrator to turn the fan on and off, provided that all of the breakers and switches leading to it are configured on as well. The remote feature was added in order to demonstrate the importance of starting the lockout/tagout task with energized equipment, then powering it down, isolating it, and locking it out to ensure that the correct breakers have been locked out.

Scheer, Jennifer↗

DOE OSTI2022

ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale

We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.

97 MATHEMATICS AND COMPUTING↗