<div class="csl-bib-body">
<div class="csl-entry">Taghizadeh, L. (2024, August 13). <i>Bayesian Inversion for Semiconductor Inverse Problems</i> [Conference Presentation]. IFIP TC7 System Modeling and Optimization (2024), Hamburg, Germany. http://hdl.handle.net/20.500.12708/199871</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/199871
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dc.description.abstract
Semiconductor devices such as nano-biosensors have many applications in our real life including medical applications for diagnostic purposes. In this work, we describe incorporating uncertainties in the mathematical modeling of semiconductor devices, as well as the propagation of uncertainties in the solution of the corresponding PDE model. We then formulate and solve a Bayesian inverse problem for the nanoscale devices. To this end, we first show that the parameter-to-observable map corresponding to this inverse problem satisfies sufficient conditions to guarantee the well-posedness properties of the proposed Bayesian inversion approach. Then, we propose a Markov-chain Monte-Carlo method for the Bayesian posterior estimation of the unknown parameters from the voltage-current measurements.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.language.iso
en
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dc.subject
Inverse problems
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dc.subject
Bayesian inversion
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dc.subject
semiconductors
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dc.title
Bayesian Inversion for Semiconductor Inverse Problems
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.relation.grantno
V 1000-N
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dc.type.category
Conference Presentation
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tuw.publication.invited
invited
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tuw.project.title
Rechnerische Unsicherheitsquantifizierung in Nanotechnologie
