Micro-Hole Drilling on Glass Substrates—A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it