#2D Semiconductor Process and Device Simulator #MicroTec: #modeling Development Status Update... https://t.co/OycWKSCKXj

#2D Semiconductor Process and Device Simulator #MicroTec: #modeling Development Status Update... https://t.co/OycWKSCKXj

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#2D Semiconductor Process and Device Simulator #MicroTec: #modeling Development Status Update… https://t.co/fZy9zm34yX

#2D Semiconductor Process and Device Simulator #MicroTec: #modeling Development Status Update https://t.co/do3ZxocZ5z http://pic.twitter.com/h11HndXXX5

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[paper] Surface Potential Equation for Low Effective Mass Channel Common Double-Gate MOSFET

Ananda Sankar Chakraborty and Santanu Mahapatra, Senior Member, IEEE

in IEEE Transactions on Electron Devices

vol. 64, no. 4, pp. 1519-1527, April 2017

doi: 10.1109/TED.2017.2661798

Abstract: Formulation of accurate yet computationally efficient surface potential equation (SPE) is the fundamental step toward developing compact models for low effective mass channel quantum well MOSFETs. In this paper, we propose a new SPE for such devices considering multisubband electron occupancy and oxide thickness asymmetry. Unlike the previous attempts, here, we adopt purely physical modeling approaches (such as without mixing the solutions from finite and infinite potential wells or using any empirical model parameter), while preserving the mathematical complexity almost at the same level. Gate capacitances calculated from the proposed SPE are shown to be in good agreement with numerical device simulation for wide range of channel thickness, effective mass, oxide thickness asymmetry, and bias voltages [read more...]

FIG: Total gate capacitance per unit width Cgg (Vg) for 7-nm-thick device with 100% asymmetry in front and back oxide thicknesses. nmax = 2. Line = model. Symbol = TCAD

levmar : Levenberg-Marquardt nonlinear least squares algorithms in C/C++

Introduction page: <http://users.ics.forth.gr/~lourakis/levmar/> 

The site provides GPL native ANSI C implementations of the Levenberg-Marquardt optimization algorithm, usable also from C++, Matlab, Perl, Python, Haskell and Tcl and explains their use. Both unconstrained and constrained (under linear equations, inequality and box constraints) Levenberg-Marquardt variants are included. The Levenberg-Marquardt (LM) algorithm is an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of nonlinear functions. It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. When the current solution is far from the correct one, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge. When the current solution is close to the correct solution, it becomes a Gauss-Newton method.

Interfaces for using levmar from high-level programming environments & languages such as Matlab, Perl Python, Haskell and Tcl are also available; please refer to the FAQ for more details.

VALint: the NEEDS Verilog-A Checker

By Xufeng Wang¹, Geoffrey Coram², Colin McAndrew³

1. Purdue University 2. Analog Devices, Inc. 3. Freescale Semiconductor

Version 1.0.0 - published on 31 Mar 2017

doi:10.4231/D3HX15S0V

Abstract: VALint is the NEEDS created, automatic Verilog-A code checker. Its purpose is to check the quality of the Verilog-A code and provide the author feedback if bad practices, common mistakes, pitfalls, or inefficiencies are found. This VALint is published as a standalone tool for the compact model community. It is also built-in as an integrated part of the NEEDS publishing platform [read more...]