Modeling aims at two goals: first it provides a tool for an engineer to optimize a device for an existing technology; secondly it should help to show the electrical behavior of a device which is not yet available. Both goals make different demands on the tool. While accuracy and efficiency are needed in the former, the latter focuses on qualitative features and should be very flexible. In the previous chapters, we discussed the physics of carrier transport in a device, using the MOSFET as a guiding example. The work we have done so far provides the physics necessary to determine the internal field and carrier distributions of the device and henceforth also its terminal currents. Casting this physics into numerical code form will enable the engineer to do his job. Provided the correct doping profiles are known, the engineer can tailor a device to suit his needs. In the first place, this would involve the question: how should I implement the process flow so that the device has the specifications required for its functioning in an electric circuit? Usually the threshold voltage, saturation currents at operating bias, subthreshold swing, transconductance, etc. are specified by the circuit environment. Many realizations satisfy the given specifications. We can single out variations that are more compatible with the process flow than others because the transistor is just one building block in the large system that constitutes the chip. To find the best choice among the remaining variations, the long-term stability of the device is considered: once the ideal device satisfies the circuit specifications, how likely is it that these might change during operation? This drift of device-specific parameters is called degradation. In a device optimization cycle the engineer tries to find the device that shows minimum degradation.


Interface State Drain Current Gate Oxide Interface Trap Oxide Charge 
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-Verlag/Wien 1991

Authors and Affiliations

  • Wilfried Hänsch
    • 1
  1. 1.CharlotteUSA

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