Bibliographic record
Abstract
On behalf of the ISPSD conference organizing committee, it is my great honor and pleasure to welcome you to the 34th IEEE International Symposium on Power Semiconductor Devices and ICs.ISPSD brings together the world's foremost experts and leading companies on power semiconductor devices and integrated circuit technology.Since the first meeting held in Tokyo in 1988, ISPSD has become the premier international forum for technical discussions on all aspects of power semiconductor devices and integrated circuits with an annual attendance of about 500 engineers, scientists and students.The conference location is rotating each year among Japan, North America, Europe and Other Areas.In 2022 the conference is returning to North America's beautiful city of Vancouver, BC Canada.The technical program committee has worked hard to bring you another excellent peer-reviewed program with short courses on Sunday, plenary sessions on Monday, and oral/poster sessions throughout the conference.The current worldwide shortage of semiconductor devices and VLSI chips, especially of power devices and power ICs, has significantly impacted the production of many products, ranging from computers, home appliances, to conventional and electric vehicles.This makes the exchange of information and ideas at ISPSD 2022 even more important and meaningful!The 2020 and 2021 conferences were forced to be conducted on-line due to the COVID-19 pandemic.The situation in 2022 is no different, with the surge in the Omicron case numbers early in the year.Our plan was forced to be revised multiple times.Luckily, we finally saw a window of opportunity to host a hybrid conference.Aside from the technical presentations, we believe that the face to face interaction among colleagues in our tightly knitted community is the most fruitful cause for attending.In order to accommodate on-line authors and attendees, we have changed ISPSD to 3 days format.Since all pre-recorded presentations will be available on-demand, we will host new roundtable sessions for a more open and extensive interaction with the on-line authors.To the inperson attendees, we are truly grateful of your presence.We hope that everyone will have a COVID-free and memorable experience in Vancouver!
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 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.327 | 0.283 |
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".