An On-Chip ESD Sensor for Use in Advanced Packaging
Bibliographic record
Abstract
Electrostatic discharge (ESD) failure results in about 35% of integrated circuit (IC) field return and causes several billion-dollar losses to the semiconductor industry per year. Most modern ICs include on-chip ESD protection circuitry which comes at the cost of I/O performance. Static charge accumulation during transport and handling may exceed the limits of this ESD protection and cause damage to the ICs. Modern advanced packaging technologies are not amenable to rework if one or more components are ESD compromised. An on-chip ESD sensor is useful in identifying and preventing the assembly of ESD compromised dielets in any advanced packaging technology. Two approaches for on-chip ESD sensor that can be employed on any die, following the ground rules within that technology are presented. Both approaches are designed, simulated, fabricated, and experimentally characterized using GlobalFoundries 22-nm fully depleted silicon-on-insulator (FDSOI) technology. While determining ESD voltage using sensors, we encounter two events that are naturally random. First, ESD is a random event with some probability distribution function (PDF) with respect to voltage. In addition, oxide breakdown within the ESD sensors is also random with a Weibull distribution. A Bayesian method is developed for the estimation of ESD voltage PDF using sensors. Experimental testing of the sensors using an ESD gun and ESD voltage estimation from the number of sensors that broke down is presented. The estimated ESD voltage closely matches the ESD voltage that the dies were subjected to during experimental verification.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".