Tool Integration for Power Device Modeling Including 3D Aspects

  • Robert W. Dutton
  • James D. Plummer


The past decade has witnessed an all explosive growth in Integrated Circuits (IC) capabilities, both for high density information processing applications and industrial purposes including sensors, actuators and power devices. The growing possibilities to create “smart” devices of unique capabilities for sensing and controlling diverse electro-mechanical (and optical) systems offer almost unlimited potential. Yet to realize these potentials, heterogeneous constraints must be quantified and used effectively in design. This talk will cover the specific issues of process, device and circuit modeling and the integration issues necessary to enable full exploitation of Technology Computer Aided Design (TCAD), tools for Power Device Modeling. The Process and Device Modeling discussions build on experience with the 2D tools SUPREM and PISCES developed at Stanford and quasi-3D applications for power devices. Both simulation and experimental results are used to illustrate key points. The growing importance of combined integrated circuit and power device applications are illustrated using several approaches to mixed mode simulation. Here the examples are broadened to include both university-based tools such as MEDUSA and SPICE/PISCES as well as commercial circuit/system level simulators. The final aspect of this contribution centers on a framework for tool integration across these various levels of design. Recent progress in establishing a TCAD framework under the auspices of the international CAD Framework Initiative (CFI) is reviewed. The paper concludes with an evaluation of progress to date in integrated modeling of power devices and a look into the future of requirements such as full 3D modeling.


Monte Carlo Central Processing Unit Metal Oxide Semiconductor Power Device Tool Integration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Robert W. Dutton
    • 1
  • James D. Plummer
  1. 1.Stanford UniversityStanfordUSA

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