REVIEW 1 cited by
Bayesian inversion for the identification of the doping profile in unipolar semiconductor devices
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
A rigorous Bayesian formulation of the inverse doping profile problem in infinite dimensions for a stationary linearized unipolar drift-diffusion model for semiconductor devices is given. The goal is to estimate the posterior probability distribution of the doping profile and to compute its posterior mean. This allows for the reconstruction of the doping profile from voltage-current measurements. The well-posedness of the Bayesian inverse problem is shown by proving boundedness and continuity properties of the semiconductor model with respect to the unknown parameter. A preconditioned Crank-Nicolson Markov chain Monte-Carlo method for the Bayesian estimation of the doping profile, using a physics-informed prior model, is proposed. The numerical results for a two-dimensional diode illustrate the efficiency of the proposed approach.
Forward citations
Cited by 1 Pith paper
-
Component-Level Inverse Design of Transmon Qubits Using Neural Networks
A tandem neural-network pipeline inversely designs cross-claw transmon layouts from target qubit frequency and anharmonicity, with 97% EM-validated usable geometries and ~56 ms CPU queries.
Discussion (0). Continue with ORCID to comment.