# Statistical Compact Model Extraction: A Neural Network Approach

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TL;DR: ANNs can model a much higher degree of nonlinearity compared to existing quadratic polynomial models and, hence, can even be used in sub-100-nm technologies to model leakage current that exponentially depends on process parameters.

Abstract: A technique for extracting statistical compact model parameters using artificial neural networks (ANNs) is proposed. ANNs can model a much higher degree of nonlinearity compared to existing quadratic polynomial models and, hence, can even be used in sub-100-nm technologies to model leakage current that exponentially depends on process parameters. Existing techniques cannot be extended to handle such exponential functions. Additionally, ANNs can handle multiple input multiple output relations very effectively. The concept applied to CMOS devices improves the efficiency and accuracy of model extraction. Results from the ANN match the ones obtained from SPICE simulators within 1%.

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### Cites methods from "Statistical Compact Model Extractio..."

...This calculation process is repeated approximately 20 000 times or more, and the calculation runs in a form similar to the Taylor series to return an accurate final value [15], [16]....

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### Cites methods from "Statistical Compact Model Extractio..."

...the correlation between process parameters increases, and the relationship between the process parameters and the electrical parameters may be non-linear, making it harder to analyze these devices with the conventional methods [8]–[11]....

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