High-Speed Removal of Thick Negative Photoresist in Advanced Packaging Applications
Bibliographic record
Abstract
ABSTRACT Negative Dry Film and Spin-On photoresists are widely used as the primary choice in wafer-scale packaging processes such as bumping, TSV, and copper pillars. High transparency of negative photoresist produces vertical profiles with no footing at low exposure energy. The result is a robust, high throughput lithography process with low cost of ownership ( Doki, 2005 ). In plating applications, negative resist provides good adhesion to a wide range of substrates, enabling high-current ECD process with good stability. Despite all the positive aspects of negative photoresists, the main challenge continues to be their removal difficulty. Aggressive remover chemistries consisting of DMSO, NMP, and TMAH have been used to swell, lift, and dissolve the material ( Moore, 2002 ). Existing equipment architectures for advanced packaging photoresist removal are based on legacy front-end platforms such as immersion bath, single-wafer spin/spray, and batch spray. There is an abundance of evidence showing the benefits of agitation in polymer dissolution ( Jack L. Koenig, 2003 ). However, there have not been any significant breakthroughs in wet strip tool design to introduce the necessary level of agitation to increase removal rate in 3DI applications. This paper demonstrates the effect of high-speed mechanical agitation in a novel immersion-based strip technology. Results show a 4X increase in removal rate compared to traditional strip tanks with N2 bubbling. Removal rates of commercially available remover chemistries have been studied on one negative dry film and two negative spin-on photoresists. Various agitation mechanisms have been evaluated.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".