Inverse Problems for Metal Oxide Semiconductor Field-Effect Transistor Contact Resistivity

Fang, Weifu; Cumberbatch, E.

doi:10.1137/0152039

Cited by 38 publications

(29 citation statements)

References 12 publications

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“…On the boundary c the Neumann boundary condition is specified with the Neumann data q(x). The Robin coefficient (x) is of practical interest in thermal problems [31] and non-destructive evaluation [32][33][34]. Here, we consider the inverse problem of estimating scalar Robin coefficients 241 from temperature measurements on the boundary c .…”

Section: Setup Of Numerical Experimentsmentioning

confidence: 99%

Fast Bayesian approach for parameter estimation

Jin

2008

Numerical Meth Engineering

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SUMMARYThis paper presents two techniques, i.e. the proper orthogonal decomposition (POD) and the stochastic collocation method (SCM), for constructing surrogate models to accelerate the Bayesian inference approach for parameter estimation problems associated with partial differential equations. POD is a model reduction technique that derives reduced-order models using an optimal problem-adapted basis to effect significant reduction of the problem size and hence computational cost. SCM is an uncertainty propagation technique that approximates the parameterized solution and reduces further forward solves to function evaluations. The utility of the techniques is assessed on the non-linear inverse problem of probabilistically calibrating scalar Robin coefficients from boundary measurements arising in the quenching process and non-destructive evaluation. A hierarchical Bayesian model that handles flexibly the regularization parameter and the noise level is employed, and the posterior state space is explored by the Markov chain Monte Carlo. The numerical results indicate that significant computational gains can be realized without sacrificing the accuracy.

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Section: Setup Of Numerical Experimentsmentioning

confidence: 99%

Fast Bayesian approach for parameter estimation

Jin

2008

Numerical Meth Engineering

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“…The Robin coe cient is closely related to the physical and chemical property of the material on the boundary. In some applications such as corrosion detection [9] and nondestructive detection of metal-oxide-silicon eld e ect transistor (MOSFET) [6], one needs to obtain the information of the material on the inaccessible boundary which naturally leads to the inverse problem of estimating the Robin coe cient on the boundary from the Cauchy data = and = on measured on the boundary . It is known that the Cauchy problem is severely ill-posed in the sense that the solution does not depend on the data continuously [2].…”

Section: Introductionmentioning

confidence: 99%

L^q -regularization for the inverse Robin problem

Fan

Jiao

et al. 2014

Journal of Inverse and Ill-Posed Problems

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In this paper, an inverse Robin problem for an elliptic equation is investigated. The Robin coe cient is assumed to be a small perturbation from a reference coe cient, hence a sparse structure is a priori known. An -regularized output least square formulation is considered with ∈ (0, 1). The existence of the minimizer is proved. The convergence of the nite element discretization with respect to mesh size is shown. A modi ed Newton algorithm is applied to solve the smoothing rst order optimality system. Two-dimensional and threedimensional numerical examples demonstrate the e ciency of the proposed method.

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“…• The inverse problem of identifying transistor contact resistivity of planar electronic devices, such as MOSFETs (metal oxide semiconductor field-effect transistors) is treated in [22]. It is shown that a one-point boundary measurement of the potential is sufficient to identify the resistivity from a one-parameter monotone family, and such identification is both stable and continuously dependent on the parameter.…”

Section: Introductionmentioning

confidence: 99%

On Inverse Problems for Semiconductor Equations

et al. 2004

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This paper is devoted to the investigation of inverse problems related to stationary drift-diffusion equations modeling semiconductor devices. In this context we analyze several identification problems corresponding to different types of measurements, where the parameter to be reconstructed is an inhomogeneity in the PDE model (doping profile). For a particular type of measurement (related to the voltage-current map) we consider special cases of drift-diffusion equations, where the inverse problems reduces to a classical inverse conductivity problem. A numerical experiment is presented for one of these special situations (linearized unipolar case).

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Inverse Problems for Metal Oxide Semiconductor Field-Effect Transistor Contact Resistivity

Cited by 38 publications

References 12 publications

Fast Bayesian approach for parameter estimation

Fast Bayesian approach for parameter estimation

L^q -regularization for the inverse Robin problem

On Inverse Problems for Semiconductor Equations

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Inverse Problems for Metal Oxide Semiconductor Field-Effect Transistor Contact Resistivity

Cited by 38 publications

References 12 publications

Fast Bayesian approach for parameter estimation

Fast Bayesian approach for parameter estimation

Lq -regularization for the inverse Robin problem

On Inverse Problems for Semiconductor Equations

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L^q -regularization for the inverse Robin problem