On-demand multi-batch self-assembly of hybrid MEMS by patterning solders of different melting points
Bibliographic record
Abstract
Self-assembly has been widely accepted as the next generation technology for integrating highly dense microelectromechanical systems (MEMS), in particular for complex and hybrid systems composed of sensing, actuating, optical, electronic, mechanical and fluidic components. In addition, some micro components may have the same material, size, shape and binding affinity, but different functions. Ideally, each micro component should bind and can only bind to a designated binding site with no recognition error even for similar sites. Due to the spontaneous nature of 'self'-assembly, challenges remain in controlling this process. In this work, a relatively simple controllable, fluid-based self-assembly method has been demonstrated, which is able to integrate hybrid MEMS in a multi-batch-wise manner. The essence of this method is to pattern solders with different melting points to designated binding sites, and to activate them separately and sequentially, even individually if needed, with appropriate processing steps at adequate temperatures. Thus, self-assembly of MEMS micro components becomes programmable.
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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 | 0.000 |
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".