Statistical circuit analysis and optimization are critical for robust nanoscale CMOS design. To accurately perform such analysis, primary process variation sources need to be identified and modeled for further circuit simulation. In this work, a rigorous method to extract process variations frominsituIV measurements is present. Transistor statistics are collected from a test chip fabricated in a 65-nm process. Gate length (L ), threshold voltage (Vth) and mobility (¿) are recognized as the leading variation sources, due to the tremendous process challenges in lithography, channel doping, and the stress engineering. To decompose these variations, three critical IV points from the cut-off and linear regions are identified. The extracted L , Vthand ¿ variations are normally distributed, with negligible spatial correlation. By including extracted variations in the nominal model file, accurate prediction of the change of drive current in all operation regions and process corners is achieved. The new extraction method guarantees excellent model matching with hardware for further statistical circuit analysis.
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Zhao et al. (2009) studied this question.
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