Novel Fast Fabrication of Nano-Structures for Sensor and Flexible Polymer Electronics
Bibliographic record
Abstract
Applications of engineered surface nano-structures span several domains. For emerging technologies such as flexible electronics and low cost sensors, the ability to produce large areas of surface nano-structures at high volume economically, can help realize several applications. One approach of achieving high volume production of surface nano-structures economically and reliably is by replication from a master stamp or template. Conventional methods in producing master stamps rely on the lengthy use of scanning-types of patterning tools, such as electron beam lithography and focused ion beam. The duration and cost of patterning limits the practicality in producing large area masters. In this thesis work, a novel method to achieve faster fabrication of master stamps containing original surface nano-structures is proposed and demonstrated. This method creates initial circular patterns of smaller dimensions than the final desired, then subsequently enlarges to the full size using lower cost processes. The time required to pattern the nano-structures through this method is reduced by 3 X compared to the conventional, and can be further optimized to find additional cost savings. The throughput improvement is particularly noticeable for nano-structured regions larger than 1 cm2. The process is correspondingly valuable for users who do not have access to the most advanced, latest and fastest, patterning tools. Exemplar replication techniques such as polymer embossing, casting, and electroforming are also demonstrated and discussed. Analysis on both the original masters and the replicated samples show good pattern transfer and excellent yield. A selected application example on an integrated sensor containing nano-optics is discussed in detail. The nano-optics sensor structures are produced at lower cost than the traditional, while still exhibiting useful sensitivity. This specific application example validate the utility of the improved master stamp fabrication process towards enabling high volume production of large area nano-structures for end applications economically. This thesis identifies the background science and challenges to current technology, discusses the motivation and objectives, methodically contemplates a solution, demonstrates novel stamp fabrication process, discusses replication techniques, examines produced structures, and provides validating application examples.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".