An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

✍ Scribed by M. S. Alam; A. Kranti; G. A. Armstrong

• Publisher: John Wiley and Sons
• Year: 2009
• Tongue: English
• Weight: 401 KB
• Volume: 22
• Category: Article
• ISSN: 0894-3370
• DOI: 10.1002/jnm.715

✦ Synopsis

The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source-drain extension, which simultaneously improves maximum frequency of oscillation f max because of lower gate to drain capacitance, and intrinsic gain A V0 ¼ g m =g ds , due to lower output conductance g ds . The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current I d on drain-source V ds and gate-source V gs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (J ds $10 mA/mm) improvement was observed in both third-order-intercept IIP 3 ($10 dBm) and intrinsic gain A V0 ($20 dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to thirdorder derivative of I d with respect to gate voltage and lower g ds in FinFET compared to bulk MOSFET.