Bibliographic record
Abstract
Microsystems are a rapidly developing technology that integrates Micro-electro-mechanical systems (commonly known as MEMS) and microfluidics devices with microelectronics and optoelectronics circuits. Due to increasing demand and opportunities from these fields, universities are introducing or have introduced MEMS/Microfluidics courses to their students at undergraduates and graduate levels. By its nature, the field is multidisciplinary and requires various backgrounds when integrating all components of Microsystems as described above. The current challenges in this area are design, fabrication and testing the microsystem devices. This brings various constraints on both the instructors and the students from the point of teaching and learning aspects. Designing a typical microsystem requires the use of modeling and simulation tools from multi energy domains (circuit simulators, multiphysics analysis, 3D modeling, fluidic flows etc). Fabrication methods have recently been evolved from traditional microelectronics fabrication techniques to more complicated micromachining technologies (bulk vs. surface micromachining, LIGA vs. Deep Reactive Ion etching etc). Testing of Microsystems goes beyond traditional electrical testing to the motion analysis of the moving parts on the microsystems. In recent years, the application of microsystems have also been moving away from traditional telecommunication and entering into new areas like health care, energy, environment, automobile and biotechnology. This makes the teaching of microsystem technologies more challenging to fulfill the needs of students entering in Microsystems from existing disciplines (electrical, mechanical, physics, etc.) with limited background on all parts of microsystems. In addition to the multidisciplinary nature of Microsystems, limited resources (small number of design platforms versus large number of students), absence of supportive funding vs. high cost of prototyping, limited time frame vs. long fabrication periods, restrictive opportunities for students to have hands-on design experience make it difficult to offer such courses at graduate and undergraduate levels. In this paper, we discuss developing a new Microsystems course curriculum with emphasis on MEMS in university environment, particularly at Queen’s University in Canada. This curriculum is designed in a team project-based manner but in modular form. The lecture will be delivered by two main instructors and some guest instructors for different topics. The team members for each project have complementary background or coming from different discipline. The pertinent resources at Queen’s University, including such in CMC Microsystems will be available to the students which solves the shortage of resources for teaching.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.041 |
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".