Bibliographic record
Abstract
Glass micromachining is currently becoming essential for the fabrication of micro-devices, including micro- optical-electro-mechanical-systems (MOEMS), miniaturized total analysis systems (μTAS) and microfluidic devices for biosensing. Moreover, glass is radio frequency (RF) transparent, making it an excellent material for sensor and energy transmission devices. Advancements are constantly being made in this field, yet machining smooth through-glass vias (TGVs) with high aspect ratio remains challenging due to poor glass machinability. As TGVs are required for several micro-devices, intensive research is being carried out on numerous glass micromachining technologies. This paper reviews established and emerging technologies for glass micro-hole drilling, describing their principles of operation and characteristics, and their advantages and disadvantages. These technologies are sorted into four machining categories: mechanical, thermal, chemical, and hybrid machining (which combines several machining methods). Achieved features by these methods are summarized in a table and presented in two graphs. We believe that this paper will be a valuable resource for researchers working in the field of glass micromachining as it provides a comprehensive review of the different glass micromachining technologies. It will be a useful guide for advancing these techniques and establishing new hybrid ones, especially since this is the first broad review in this field.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".