Coupled process and device modeling of Cu(In,Ga)Se<sub>2</sub> solar cells

Xiang, Xiaofeng; Sommer, David E.; Gehrke, Aaron S.; Dunham, Scott T.

doi:10.1109/pvsc43889.2021.9519020

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…Accurate predictions for deep-level defects necessitate theoretical calculations and experimental evidence, which the ML model cannot provide at current stage. Additionally, the model’s consideration is primarily focused on substitutional defects and interstitials, overlooking other critical point defect types, such as defect complexes, , which can significantly impact semiconductor devices. To enhance the framework, a more comprehensive defect data set is required.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, it is important to acknowledge that the “frozen-in” approximation might not hold in certain cases, particularly when the interactions between different defects are significant, like vacancy-interstitial pair annihilation and exchange reactions . Also, the strong Coulomb interaction between defects with opposite charges can give rise to defect complexes, , a consideration not addressed within the current framework.…”

Section: Methodsmentioning

confidence: 99%

“…Point defects play a crucial role in semiconductor devices as they can significantly affect device performance by facilitating atomistic diffusion 1 or creating trapping centers. 2 These defects can be categorized into two types on the basis of their transition levels relative to the valence band maximum (VBM) and conduction band minimum (CBM): shallow defects and deep level defects. Low concentrations of shallow level defects generally have minimal impact on device performance, whereas extrinsic concentrations of shallow defects are desirable dopants as they can be effectively ionized to control p-type or n-type doping.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Point defects play a crucial role in semiconductor devices as they can significantly affect device performance by facilitating atomistic diffusion or creating trapping centers . These defects can be categorized into two types on the basis of their transition levels relative to the valence band maximum (VBM) and conduction band minimum (CBM): shallow defects and deep level defects.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Xiang,

Soh,

Dunham

2024

J. Phys. Chem. C

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In this work, we present an exploration of deep learning models for predicting defect properties in cubic phase semiconductors. The nature of impurity energy levels strongly influences the performance of semiconductors in a wide range of applications, such as solar cells, field effect transistors, and qubits for quantum computing. In this work, we employ two types of deep learning models, a crystal defect graph neural network and a chemical environment-encoded artificial neural network, to predict defect properties. The models are trained on a data set of charge-dependent defect formation energies obtained from density functional theory computations and descriptors based on elemental properties, defect local environment, and relevant semiconductor properties. We assess the models' performance and showcase their capability in optimizing semiconductor devices, particularly when used in tandem with compositionally constrained thermodynamics and technology computer-aided design models.

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Section: Resultsmentioning

confidence: 99%