Fabrication of high aspect ratio (HAR) atomic force microscope (AFM) tips and nano-pillar arrays using pseudo-Bosch and wet etching
Bibliographic record
Abstract
This thesis is mainly focused on the research in the field of nanofabrication and application related to atomic force microscope (AFM) system. AFM tip is the most important part of AFM imaging to obtain the surface information by touching the substrate and “feeling” the force interaction. However, the scanned image using regular commercial tips is not accurate when scanning across areas with deep/tall and narrow structures. To overcome the drawbacks, high aspect ratio (HAR) tip was introduced to obtain high-quality and accurate images with high resolution and clear restoration. Several common fabrication techniques of HAR AFM tip including focus ion beam milling, electron/ion beam induced deposition, carbon nanotube tips and Nauganeedle method are reviewed, but batch production is hard to achieved due to the tips are fabricated one by one in these methods. Here we present a novel method based on dry etching to fabricate HAR AFM tips with high throughput, and some important steps such as electron beam lithography and etching process during the fabrication will also be discussed in this work. \n \nThe long-term goal of dry etching for our purpose is to have high etching rate, high etching selectivity to mask material, and controllable vertical profile with smooth sidewall, so non-switching (i.e. introduce SF6 and C4F8 gas into the chamber simultaneously) pseudo-Bosch recipe was developed and optimized, to replace the standard Bosch process that gives wavy and rough sidewall. Moreover, when switching between SF6/C4F8 etching and O2 cleaning, that is, adding periodic oxygen (O2) plasma cleaning step to the pseudo-Bosch etching process, the etching rate of silicon structures can be significantly improved without any adverse effect. The obtained profile of pillars is slightly positively tapered which is optimal for HAR tips. \n \nThe fabrication of HAR tips is similar to that of HAR nano-pillars, and the same nano-pillars can be extended to the biomedical application of nanoneedles. As such, here we present a new method to fabricate ultra-high aspect ratio silicon nano-pillar arrays using reactive ion etching and subsequent sharpening/thinning down by wet etching, which features high viability and high throughput. The results show that the aspect ratio of the fabricated nano-pillars can be up to 125.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".