Abstract

Radiation-induced defects can have a significant impact on the longevity and performance of semiconductor devices. We present an Atomistically Informed Device Engineering (AIDE) method that integrates first-principles defect properties and experimentally measured parameters into a device model to dynamically simulate the defect chemistry in semiconductors. For a silicon-doped gallium arsenide (GaAs) material, we showcase three capabilities: (i) Fermi level movement including its component electron and hole Fermi levels, (ii) dynamical charge equilibration with the arsenic vacancy serving as an example, and a (iii) diffusion-driven reaction between Coulomb attracted gallium interstitial (Gai) and arsenic vacancy (vAs). Governed by charge carrier reactions, the electron and hole Fermi levels remained dissimilar until equilibrium was achieved at EF ≈1.32 eV. The equilibrium Fermi level was verified by successfully identifying v3−As as the most populated charge state within the arsenic vacancy defect. Lastly, a Coulomb attraction, created by the shifted Fermi level and the charge equilibration process, between Ga₁+i and v3−As resulted in the formation of a doubly negative gallium antisite (Ga2−As). The AIDE method can access experimentally inaccessible short-time and low-concentration regimes, is generalizable to other more complex systems (e.g., indium gallium arsenide), and, after solving open problems in GaAs, will serve as a virtual experiment to bound estimates for difficult-to-measure physical quantities.

Abstract

Radiation-induced defects can have a significant impact on the longevity and performance of semiconductor devices. We present an Atomistically Informed Device Engineering (AIDE) method that integrates first-principles defect properties and experimentally measured parameters into a device model to dynamically simulate the defect chemistry in semiconductors. For a silicon-doped gallium arsenide (GaAs) material, we showcase three capabilities: (i) Fermi level movement including its component electron and hole Fermi levels, (ii) dynamical charge equilibration with the arsenic vacancy serving as an example, and a (iii) diffusion-driven reaction between Coulomb attracted gallium interstitial (Gai) and arsenic vacancy (vAs). Governed by charge carrier reactions, the electron and hole Fermi levels remained dissimilar until equilibrium was achieved at EF ≈1.32 eV. The equilibrium Fermi level was verified by successfully identifying v3−As as the most populated charge state within the arsenic vacancy defect. Lastly, a Coulomb attraction, created by the shifted Fermi level and the charge equilibration process, between Ga₁+i and v3−As resulted in the formation of a doubly negative gallium antisite (Ga2−As). The AIDE method can access experimentally inaccessible short-time and low-concentration regimes, is generalizable to other more complex systems (e.g., indium gallium arsenide), and, after solving open problems in GaAs, will serve as a virtual experiment to bound estimates for difficult-to-measure physical quantities.

Abstract

Radiation-induced defects can have a significant impact on the longevity and performance of semiconductor devices. We present an Atomistically Informed Device Engineering (AIDE) method that integrates first-principles defect properties and experimentally measured parameters into a device model to dynamically simulate the defect chemistry in semiconductors. For a silicon-doped gallium arsenide (GaAs) material, we showcase three capabilities: (i) Fermi level movement including its component electron and hole Fermi levels, (ii) dynamical charge equilibration with the arsenic vacancy serving as an example, and a (iii) diffusion-driven reaction between Coulomb attracted gallium interstitial (Gai) and arsenic vacancy (vAs). Governed by charge carrier reactions, the electron and hole Fermi levels remained dissimilar until equilibrium was achieved at EF ≈1.32 eV. The equilibrium Fermi level was verified by successfully identifying v3−As as the most populated charge state within the arsenic vacancy defect. Lastly, a Coulomb attraction, created by the shifted Fermi level and the charge equilibration process, between Ga₁+i and v3−As resulted in the formation of a doubly negative gallium antisite (Ga2−As). The AIDE method can access experimentally inaccessible short-time and low-concentration regimes, is generalizable to other more complex systems (e.g., indium gallium arsenide), and, after solving open problems in GaAs, will serve as a virtual experiment to bound estimates for difficult-to-measure physical quantities.

Abstract

Radiation-induced defects can have a significant impact on the longevity and performance of semiconductor devices. We present an Atomistically Informed Device Engineering (AIDE) method that integrates first-principles defect properties and experimentally measured parameters into a device model to dynamically simulate the defect chemistry in semiconductors. For a silicon-doped gallium arsenide (GaAs) material, we showcase three capabilities: (i) Fermi level movement including its component electron and hole Fermi levels, (ii) dynamical charge equilibration with the arsenic vacancy serving as an example, and a (iii) diffusion-driven reaction between Coulomb attracted gallium interstitial (Gai) and arsenic vacancy (vAs). Governed by charge carrier reactions, the electron and hole Fermi levels remained dissimilar until equilibrium was achieved at EF ≈1.32 eV. The equilibrium Fermi level was verified by successfully identifying v3−As as the most populated charge state within the arsenic vacancy defect. Lastly, a Coulomb attraction, created by the shifted Fermi level and the charge equilibration process, between Ga₁+i and v3−As resulted in the formation of a doubly negative gallium antisite (Ga2−As). The AIDE method can access experimentally inaccessible short-time and low-concentration regimes, is generalizable to other more complex systems (e.g., indium gallium arsenide), and, after solving open problems in GaAs, will serve as a virtual experiment to bound estimates for difficult-to-measure physical quantities.