3 research outputs found
Efficient Parameters Selection for CNTFET Modelling Using Artificial Neural Networks
Abstract In this article different types of artificial neural networks (ANN) were used for CNTFET (carbon nanotube transistors) simulation. CNTFET is one of the most likely alternatives to silicon transistors due to its excellent electronic properties. In determining the accurate output drain current of CNTFET, time lapsed and accuracy of different simulation methods were compared. The training data for ANNs were obtained by numerical ballistic FETToy model which is not directly applicable in circuit simulators like HSPICE. The ANN models were simulated in MATLAB R2010a software. In order to achieve more effective and consistent features, the UTA method was used and the overall performance of the models was tested in MATLAB. Finally the fast and accurate structure was introduced as a sub circuit for implementation in HSPICE simulator and then the implemented model was used to simulate a current source and an inverter circuit. Results indicate that the proposed ANN model is suitable for nanoscale circuits to be used in simulators like HSPICE
Recommended from our members
Device and circuit-level models for carbon nanotube and graphene nanoribbon transistors
Metal-oxide semiconductor field-effect transistor (MOSFET) scaling throughout the years has enabled us to pack million of MOS transistors on a single chip to keep in pace with Moore’s Law. After forty years of advances in integrated circuit (IC) technology, the scaling of silicon (Si) MOSFET has entered the nanometer dimension with the introduction of 90 nm high volume manufacturing in 2004. The latest technological advancement has led to a low power, high-density and high-speed generation of processor. Nevertheless, the scaling of the Si MOSFET below 22 nm may soon meet its’ fundamental physical limitations. This threshold makes the possible use of novel devices and structures such as carbon nanotube field-effect transistors (CNTFETs) and graphene nanoribbon field-effect transistors (GNRFETs) for future nanoelectronics. The investigation explores the potential of these amazing carbon structures that exceed MOSFET capabilities in term of speed, scalability and power consumption. The research findings demonstrate the potential integration of carbon based technology into existing ICs. In particular, a simulation program with integrated circuit emphasis (SPICE) model for CNTFET and GNRFET in digital logic applications is presented. The device performance of these circuit models and their design layout are then compared to 45 nm and 90 nm MOSFET for benchmarking. It is revealed through the investigation that CNT and GNR channels can overcome the limitations imposed by Si channel length scaling and associated short channel effects while consuming smaller channel area at higher current density