Plasmasens: Using Cold Atmospheric Plasmas to Transform Thin Film Manufacturing Blind Spots into Rich, Multi-Layered Insights
Bibliographic record
Abstract
Thin films, or micro- and/or nano-materials with unique functional properties, enable many clean energy industries, including batteries, fuel cells, solar cells, and carbon conversion. Economical, high yield, minimal-waste manufacturing of high-performance thin films will thus be vital for achieving society’s net-zero carbon emission goals. As renewable energy demand (and consequently thin film manufacturing) continues to scale, thin film metrology becomes exponentially more challenging since it is not possible to check the key properties of each thin film at each processing step without compromising throughput. A real-time, in-line, multi-measurement thin film metrology strategy has the potential to unlock unprecedented opportunities for implementing smart manufacturing practices and advanced process control solutions to accelerate process optimization and yield ramp, reduce material waste, and realize higher precision and production throughput. SirenOpt has invented a novel thin film metrology platform, PlasmaSens TM , capable of non-destructively measuring multiple thin film properties simultaneously, in real-time, and inside of high-volume manufacturing lines. More specifically, PlasmaSens leverages cold atmospheric plasma and physics-informed machine learning to non-destructively create uniquely distinctive, multifaceted material fingerprints in real-time, thus transforming measurement blind spots into rich multi-layered material insights. SirenOpt is currently deploying beta versions of its PlasmaSens platform to industrial manufacturers and research institutions in the US, Canada, Europe, Japan, and Taiwan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".