A trap‐corrected bias–temperature–stress (TraC‐BTS) method to quantify the kinetics of ion migration in dielectrics based on capacitance–voltage measurements is presented. The method is based on the extraction of flatband potential (Vfb) shifts in metal–insulator–semiconductor test structures an enables the reliability assessment of semiconductor dielectrics and solar cells. Herein, it is shown that carrier trapping in the dielectric must be accounted for, as it strongly affects the measurement of flatband potential in silicon‐nitride‐based capacitors. This effect is corrected by isolating the contribution of trapping on Vfb using contamination‐free control devices. A specific drift‐diffusion model of the ion kinetics presented herein allows the extraction of ion diffusivity. An Arrhenius relationship is obtained for sodium diffusivity in silicon nitride in a temperature range from 30 °C to 90 °C at an electric field of 1 MV cm−1, yielding a prefactor and an activation energy , with a 95% confidence interval of [] eV for the diffusivity. These quantitative kinetics confirm that silicon nitride may be a poor sodium migration barrier under a significant electric field.

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The presence of trace alkali ions, especially sodium (Na þ ), in semiconductor devices has long been identified as a cause of device instability due to ion-drift-induced voltage screening under operating temperatures and applied potentials. [1][2][3][4][5] In the last decade, it has been established that sodium contamination in silicon photovoltaic (PV) modules leads to a catastrophic power loss known as potential-induced degradation (PID), [6][7][8][9] which can lead to failure within a timescale of a few hours to several days under accelerated testing. [10][11][12][13][14][15] A critical component of PID is the transport of Na ions across the dielectric-Si interface, ultimately leading to shunting of the p-n junction. In contrast, the possibility of harnessing ion transport in dielectrics has attracted increasing interest for applications in neuromorphic computing. [16][17][18] Establishing rigorous models for these ion transport phenomena is a necessary step toward comprehensive control of mobile ions in established and novel semiconductor device architectures.Historically, electrostatic models of mobile charges in metal-insulatorsemiconductor (MIS) structures have had some success in reproducing phenomenological effects. [4,[19][20][21] Among these models, Snow et al. [19] proposed an analytical approximation to ion transport in MIS structures, which adequately describes electrostatic measurements of the image