Abstract
As critical transistor dimensions scale below the 100nm (nanoscale) regime, quantum mechanical (QM) effects begin to manifest themselves and affect important device performance metrics. Therefore, simulation tools which can be applied to design nanoscale transistors in the future, require new theory and modeling techniques that capture the physics of quantum transport accurately and efficiently. In this paper, we apply an artificial neural network (ANN) to the study of the nanoscale CMOS circuits. The latter is based on the 2-D numerical non-equilibrium Green’s function (NEGF) simulation of the current–voltage characteristics of an undoped symmetric DG MOSFET. The encouraging comparisons between numerical results and ANN PSPICE simulations have indicated that the developed ANN subcircuit representation particularly suitable to be incorporated in SPICE-like tools for nanoscale CMOS circuits simulation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
