A Fast TCAD-based Methodology for Variation Analysis of Emerging Nano-Devices Hassan Ghasemzadeh Mohammadi∗ , Pierre-Emmanuel Gaillardon∗ , Majid Yazdani† , Giovanni De Micheli∗ Integrated Systems Laboratory∗ ,

First Page		Document Content
Date: 2015-08-19 06:57:44 Econometrics Electronic design automation Computational neuroscience Computational statistics Operations research Simulation Computer simulation Artificial neural network Regression analysis Linear regression Prediction Synopsys		A Fast TCAD-based Methodology for Variation Analysis of Emerging Nano-Devices Hassan Ghasemzadeh Mohammadi∗ , Pierre-Emmanuel Gaillardon∗ , Majid Yazdani† , Giovanni De Micheli∗ Integrated Systems Laboratory∗ , Add to Reading List Source URL: majid.yazdani.me Download Document from Source Website File Size: 1,77 MB Share Document on Facebook

	An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine DocID: 1vflA - View Document
	Neural networks Feedforward neural network - artificial neuron September Abstract6, 2012 DocID: 1tmtK - View Document
	Neural networks Feedforward neural network - multilayer neural network ARTIFICIAL NEURON 1 DocID: 1tkaU - View Document
	Neural networks Feedforward neural network - capacity of neural network ARTIFICIAL NEURON 1 DocID: 1tfkQ - View Document
	Genome scale classification of recombinant and non-recombinant HIV-1 sequences using artificial neural network ensembles Dr. Ashok Kumar Dwivedi, Assistant Professor, Department of Bioinformatics, Mathematics and Comp DocID: 1s7Nx - View Document