(Invited) Plasma Processes for Emerging Silicon-Based MEMS, NEMS and Packaging Applications
Bibliographic record
Abstract
Plasma processes are important for emerging silicon-based micro-electro-mechanical systems (MEMS), nano-electro-mechanical systems (NEMS) and packaging applications. Typically every device has a customised process flow but synergies can be found as most are produced using a 2.5D approach comprising sequential deposition, lithography and etch cycles. Plasma processes are critical in enabling reproducible dry deposition and etch steps during the manufacturing flow of MEMS and NEMS devices but individual tools need the flexibility to address multiple different applications and process requirements. Increasingly plasma processes are also being adopted in nano-imprint lithography and bonding process steps. Plasma processes may be divided into the following broad families: chemical vapour deposition (CVD), atomic layer deposition (ALD), physical vapour deposition (PVD), surface functionalisation, etch (physically-driven) and etch (chemically-driven). The merits of each plasma technique and their application to emerging devices are discussed – including silicon etch processes that are at the heart of most MEMS and NEMS devices. Here trends are towards higher aspect ratios for enhanced performance and smaller footprint whilst maintaining throughput. Increasingly, large cavities are also being etched to realise membrane-based devices and in multi-wafer stacks where through silicon vias (TSVs) are emerging as a key technology for interconnect. At the research stage, the integration of ALD into MEMS is becoming more common whilst in NEMS devices are taking advantage novel combinations of processes and materials to realise new functions.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".