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Marga Alisjahbana
Institut Teknologi Indonesia
Computer Science & IT

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Statistical MOSFET Parameter Extraction with Parameter Selection for Minimal Point Measurement Marga Alisjahbana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 3: September 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i3.1129

Abstract

A method to statistically extract MOSFET model parameters from a minimal number of transistor I(V) characteristic curve measurements, taken during fabrication process monitoring. It includes a sensitivity analysis of the model, test/measurement point selection, and a parameter extraction experiment on the process data. The actual extraction is based on a linear error model, the sensitivity of the MOSFET model with respect to the parameters, and Newton-Raphson iterations. Simulated results showed good accuracy of parameter extraction and I(V) curve fit for parameter deviations of up 20% from nominal values, including for a process shift of 10% from nominal.
Statistical MOSFET Parameter Extraction with Parameter Selection for Minimal Point Measurement Marga Alisjahbana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 3: September 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i3.984

Abstract

A method to statistically extract MOSFET model parameters from a minimal number of transistor I(V) characteristic curve measurements, taken during fabrication process monitoring. It includes a sensitivity analysis of the model, test/measurement point selection, and a parameter extraction experiment on the process data. The actual extraction is based on a linear error model, the sensitivity of the MOSFET model with respect to the parameters, and Newton-Raphson iterations. Simulated results showed good accuracy of parameter extraction and I(V) curve fit for parameter deviations of up 20% from nominal values, including for a process shift of 10% from nominal.
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