From Femtoseconds to Gigaseconds: The SolDeg Platform for the Performance Degradation Analysis of Silicon Heterojunction Solar Cells

Abstract

Heterojunction Si solar cells exhibit notable performance degradation. We modeled this degradation by electronic defects getting generated by thermal activation across energy barriers over time. To analyze the physics of this degradation, we developed the SolDeg platform to simulate the dynamics of electronic defect generation. First, femtosecond molecular dynamics simulations were performed to create a-Si/c-Si stacks, using the machine learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster method. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time-dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si heterojunction solar cells via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution and determined the time-dependent defect density N(t) from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation N(t) over at least 10 orders of magnitude in time. (4) We found that in relative terms, V oc degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the time correspondence curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times t normal ∝ t accel T(accel)/T(normal) . ( 6) We also carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times that slowed considerably at longer time scales.

Hydrogen-induced degradation dynamics in silicon heterojunction solar cells via machine learning

“…Si HJ stacks: structure, energies and defects. As mentioned earlier, in a previous paper, we simulated the degradation dynamics in simplified, Si-only c-Si/a-Si stacks 14 . In that paper, we used the machine learning-based GAP for the Si-Si interatomic potential.…”

“…From here on, we extend the scope of the SolDeg simulator of Ref. 14 to refer to the here-described multiscale, hierarchical combination of simulations and analytical methods. Figure 4 shows our SolDegsimulated N(t) alongside the experimentally determined N(t) from Fig.…”

“…To adopt a pragmatic convention, we took IPR values exceeding 0.1 as an indicator that the electronic wavefunction was substantially localized on one or just a few atoms. We have described our IPR method in great detail in a recent paper 14 . Using this IPR method, we determined the change in the number of localized defects when a hydrogen atom broke away from a Si-H bond and moved to an interstitial Si-Si bond center.…”

“…Although a-Si:H has been analyzed for more than 40 years, several questions about its physics still remain open. Unresolved issues include understanding the classes of structural and electronic defects [4][5][6][7][8][9][10][11] , their formation and statistical distributions, and the long-term structural defect dynamics of a-Si:H. These issues all play crucial roles in the degradation of the crystalline/ amorphous (c-Si/a-Si:H) interface of Si HJs [12][13][14] .…”

“…The DB density can increase by different mechanisms. In a previous paper, we computed the increase of the DB density by the structural evolution of the Si matrix alone 14 . We found that the structural evolution of the Si matrix did increase the DB density, but quantitatively was not sufficient to account for the data.…”