Fabrication of Thickness Modulated Multilayer Thin Film Optical Filters for Color Rendering in Photovoltaics Using Ecr-PECVD and Magnetron Sputtering
Bibliographic record
Abstract
Electric vehicles’ usage can reduce greenhouse gas emissions and their adverse health effects on humans. But we can only utilize the full environmental benefits of them when charging is done using renewable energy sources with zero or low carbon emissions. Researchers have suggested integrating low-cost, flexible, and thin-film copper indium gallium selenide solar cells directly onto all the upward-facing body parts of electric vehicles [1]. However, this integration comes with an aesthetic drawback. We are proposing replacing the anti-reflective coating present in standard solar cells with a notch filter (a narrow high-reflection region in the visible range along with high transmission for the rest of the solar spectrum) to give a distinct color rendering to solar-charged electric vehicles. Notch filters can be designed using a rugate filter structure consisting of continuously modulated refractive indices or using a repetitive two-material stack of alternating high (H) and low (L) refractive index materials with precise thickness, also known as the standard two-material technology (S2MT). Using OptiLayer and MATLAB, we designed our S2MT thickness modulated structures to obtain blue (400 nm), green (550 nm), and red (632 nm) colors with minimum number of layers. Layer numbers in each design range between 2 – 12 depending on the chosen material pair and the targeted color, thus achieving simplicity and effectiveness. We used gradual evolution method followed by thin layer removal to obtain our thickness modulated multilayer optical filter designs. We simulated our optical filter structures for various high and low refractive index material pairs, and a detailed analysis of our findings will be presented. In this analysis, we studied the effect of refractive index ratios (high refractive index to low refractive index) on the current response of the colorful solar cells and our merit function. This study helped us to understand the optimal material pair range suitable to our application. Our designs were fabricated using magnetron sputtering and electron cyclotron resonance plasma-enhanced chemical vapor deposition (ECR-PECVD) and validated through variable angle spectroscopic ellipsometry and reflectance spectroscopy, showing strong agreement with our simulations. We fabricated these optical filters with three different material pairs using ECR-PECVD (SiN-SiON, refractive index ratio of 1.38 [2]) and magnetron sputtering (Nb2O5-SiO2, with refractive index ratio of 1.71 [3] and Al2O3-SiO2, with a refractive index ratio of 1.21). With a minimal trade-off in the functionality and efficiency of solar cells, we were successfully able to transform standard solar cells into aesthetically pleasing components, broadening their appeal and potential applications in consumer products. A detailed comparison of our simulation and fabrication results will be presented. Figure 1: Observed color of the fabricated (a) blue, (b) green, and (c) red filter on silicon wafer using a solar simulator under 1 sun condition. [3] References: [1] M. H. Mobarak, R. N. Kleiman, and J. Bauman, “Solar-Charged Electric Vehicles: A Comprehensive Analysis of Grid, Driver, and Environmental Benefits,” IEEE Transactions on Transportation Electrification , vol. 7, no. 2, pp. 579–603, Jun. 2021, doi: 10.1109/TTE.2020.2996363. [2] P. Bhattacharyya, B. Ahammou, F. Azmi, R. Kleiman, and P. Mascher, “Design and fabrication of color-generating nitride based thin-film optical filters for photovoltaic applications,” Journal of Vacuum Science & Technology A , vol. 41, no. 3, May 2023, doi: 10.1116/6.0002357. [3] P. Bhattacharyya et al. , “Design method for generating multiple colors with thickness-modulated thin-film optical filters for silicon solar cells,” Journal of Vacuum Science & Technology A , vol. 43, no. 1, Jan. 2025, doi: 10.1116/6.0003977. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".