Inkjet Photoresist Printing for Semiconductor Devices
Bibliographic record
Abstract
The current industry-dominant method for photoresist deposition is spin-coating. However, as technology continues to advance, specifically in the field of microelectronic mechanical systems (MEMS), the limitations of spin-coating are becoming more apparent. A promising alternative is inkjet printing; a well-developed technology that has been very successful in a multitude of other applications, notably in 3D printing. Aside from offering good results on patterned wafers – filling cavities and eliminating edge beading – this additive technique also holds the advantage of dispensing minute volumes of photoresist, unlike spin-coating, which ejects larger volumes of resist while only 5% to 10% is retained on the substrate. Additionally, it can selectively deposit resist onto required locations on the wafer or directly print patterns, reducing the number of photolithography steps required. This article focuses on developing a process for printing photoresist for photolithography applications using a commercially available SUSS inkjet printer in a manufacturing, cleanroom environment (200 mm Fab, FED STD 209E class 10). A commercially available positive photoresist used for production within C2MI’s facility – whose intended method of deposition is spin-coating – was adapted through dilutions for inkjet printing. The findings included an optimal viscosity within a range of 5.5 cPs to 7.5 cPs (with a ± 1 cPs tolerance) and a surface tension between 28 mN/m and 34 mN/m. A 4.35% coating uniformity on a 1.48 μm thick layer was achieved, on average. Furthermore, sharp printed edges without bleeding were obtained, with a print time of 62 seconds for a blanket print. A simple three-layer proof-of-concept test structure was then designed and fabricated to compare inkjet printing with traditional spin-coating photolithography. Uniformity, thickness, print time, resist volume, and the critical dimension (CD) of the photoresist coatings after photolithography were measured and compared, demonstrating promising results for inkjet printing. Although more work is still needed to bring inkjet printing from a proof-of-concept method to an industry ready technology, the control and precision offered by inkjet printing can lead to advancements in material deposition for a variety of applications in the microfabrication process of MEMS. It has the potential to overcome current limitations of spin-coating and can do so while being a more eco- responsible and cost-effective option by reducing material waste.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.022 |
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".