Bibliographic record
Abstract
ABSTRACT Light emitting diodes, LEDs, have evolved tremendously in the last few years. They are no longer relegated to such roles as low output power indicator lights on panels, or seasonal decorative light strings. Just as electronics has infiltrated every aspect of our lives, a LED invasion is aggressively underfoot. Yet the technology behind these devices is poorly understood, often leading to avoidable early failures. In this webinar we will look at every aspect of LED technology. First we will explore the semiconductor devices themselves and the structures within that allow them to be efficient emitters. Next we will focus on high brightness LEDs and review their packaging and assembly challenges. Then we will investigate LEDs in luminaires: the phosphor materials of the LED devices, LED driving circuits, dimming issues. Finally, the long term reliability issues and failure mechanisms of LED devices will be reviewed. Topic Outline: Introduction What are LEDs History Physics of the semiconductor die Band diagrams Types of LEDs Quantum wells Contact metallurgy Packaging Heat, heat, heat Lens or no lens Phosphors A19 format Reliability consideration Performance LM79 and LM80 Failure modes Who should see this? This webinar presents an overview of current LED technology for design engineers, component engineers, quality engineers and their managers who may not be familiar with the physics, internal structure and reliability issues of LEDs as components. Presenter's Bio Dr. Martine Simard-Normandin, President, MuAnalysis Inc. Dr. Simard-Normandin has over 30 years experience in microelectronics, specializing in semiconductor device physics, reverse engineering and electrical and material characterization. She has authored or co-authored more than 50 scientific journal and conference papers on microanalysis. Dr. Simard-Normandin holds a B.Sc. in physics from the Universite de Montreal, a M.Sc. and Ph.D. in astronomy from the University of Toronto. She was awarded an Industrial Postdoctoral Fellowship from the American Physical Society, focusing on microelectronics, and recently the prestigious Medal of the Faculty of Arts and Sciences of the Universite de Montreal. Dr. Simard-Normandin has held the positions of Manager - Materials and Device Analysis at STMicroelectronics- Centre for Microanalysis and Manager of Materials and Structures Analysis at Nortel Networks. In 2002 she founded MuAnalysis Inc., a privately-owned Canadian company offering expertise in failure analysis, materials analysis and reliability testing. On Demand Webinar
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".