# Sequential Quenching to Predict Semiconductor Defect Concentrations from Formation & Migration Energies: The Case of CdTe:As Doping

Source: [Sequential Quenching to Predict Semiconductor Defect Concentrations from Formation & Migration Energies: The Case of CdTe:As Doping](<https://www.sota2.com/research/paper/69b8e01f0636e5bf4afbeba7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-10T05:00:46.690Z

Authors: Khandakar Aaditta Arnab, Intuon Chatratin, Anderson Janotti, Michael Scarpulla

Published: 2026-03-15T21:24:16.000Z

## Abstract

Defect concentrations in semiconductors are strongly influenced by thermal history during growth and cooldown, yet most defect calculations assume either instantaneous quenching from high temperature or that full-equilibrium is maintained - two limiting cases rarely approached in reality. Here, we introduce sequential quenching (SQ) as a 3rd type of defect calculation utilizing defect formation and migration energies to model defect concentrations subject to diffusion-limited kinetics in samples cooled at finite rates. In SQ, the concentration of each defect is frozen at a characteristic temperature determined by its diffusion rate, distance to sources/sinks, and cooling rate. Because different charge-states interact through charge neutrality but freeze at different temperatures, the sequence of freeze-in events is non-commuting. Critically, not all room-temperature SQ solutions can be predicted from full equilibrium (EQ) or full-quenching (FQ) calculations - erroneous predictions are likely without SQ. We illustrate SQ using the example of As-doped CdTe, for which experimental data show differences in doping with cooling rate and between polycrystalline thin-films for photovoltaics and bulk crystals. SQ calculations reveal that fast-diffusing defects such as Cd-interstitials remain mobile to lower temperatures and freeze-in at larger characteristic distances, leading to strong compensation and n-type behavior in rapidly cooled or bulk samples. Slower cooling and reduced characteristic distances suppress donor freeze-in and enhance p-type activation. These results establish SQ as a physically transparent and computationally efficient framework for connecting cooling conditions, sample geometry, and defect kinetics to dopant activation in CdTe and related materials.

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.14618>)

[PDF](<https://arxiv.org/pdf/2603.14618.pdf>)