Description
Impact ionization plays a central role in avalanche multiplication, electrical breakdown and high-field carrier transport in semiconductor devices. However, accurate prediction of impact-ionization behaviour typically requires computationally intensive first-principles calculations involving wavefunction overlap integrals, dielectric screening and high-dimensional Brillouin-zone integrations. These requirements have largely limited the application of impact-ionization modelling to small numbers of materials and hindered its use in large-scale materials discovery.
We present a computationally tractable framework for predicting impact-ionization characteristics using only publicly available electronic structure data from the Materials Project database. The framework comprises a hierarchy of physics-based proxies, including kinematic threshold optimisation, phase-space models, phase-space evaluation using reconstructed Brillouin-zone manifolds, and physically motivated scattering proxies incorporating momentum-transfer-dependent matrix elements. Together, these approaches progressively recover key aspects of impact-ionization physics while avoiding explicit wavefunction calculations.
Benchmark calculations on representative semiconductor materials demonstrate how impact-ionization thresholds, accessible phase space and scattering descriptors evolve across the hierarchy of models. The results reveal a clear distinction between the minimum kinematically allowed impact-ionization threshold and the energy at which significant impact-ionization phase space becomes available, highlighting the important role of Brillouin-zone topology in avalanche processes.
By leveraging pre-computed electronic structure databases, the proposed framework enables scalable screening of semiconductor materials for avalanche photodiodes, radiation detectors and high-field electronic devices. The methodology provides a practical pathway for incorporating impact-ionization descriptors into data-driven semiconductor materials discovery workflows while substantially reducing the computational cost associated with conventional first-principles approaches.
| I am the presenting author | Yes |
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