Predicting solar cell efficiencies using historical data from a manufacturing process
Bibliographic record
Abstract
Abstract The solar cell manufacturing data of a passivated emitter and rear cell solar cell manufacturing plant was studied to assess the effects of tool usage and the processing time spent on each tool on the solar cell efficiency. Since manufacturing processes involve several steps with multiple tools, tracing their quality parameters back to the tool usage is difficult—most plants measure parameters at the end of the manufacturing process. We used multilinear regression, partial least squares (PLS), kernel based PLS, random forest, gradient boosted decision trees (GBDTs), and 2D convolutional neural network (CNN) models to study the variation of the cell efficiencies with variations in tools and tool processing times. We evaluated our models' performance using mean squared error (MSE) and the coefficient of determination. The GBDT had the best coefficient of determination, but with a relatively higher MSE for efficiency prediction using batch processing times. Shapley analysis was used to study the effect of the frequency of tool usage on efficiency prediction and identify process steps and tools having maximum impact on efficiency. This work can be used as a basis to selectively utilize tools in the solar cell manufacturing process that will result in better solar cell quality (or reduce material wastage due to poor quality), thereby making solar cells more affordable and hence more readily adoptable. It can also be translated to other industries whose manufacturing processes involve multiple tools/steps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".