Bibliographic record
Abstract
Electrostatic discharge (ESD) is one of the most important failure mechanisms of integrated circuits (ICs).ESD can damage ICs during manufacture, assembly of the component on the printed circuit boards and use in the field as part of a system.Therefore, an adequate ESD protection is required to improve yield and to reduce field return due to ESD damage, consequently, it is necessary that all ICs are protected against ESD.However, ESD protection can adversely cause degradation of IC performance, particularly on radio frequency (RF) ICs.These types of ICs are the most affected by the introduction of ESD protection, which cause the degradation of RF parameters.As a result, reduction of the RF performance degradation is highly desired and was the focus of this study.Two LNAs, one with ESD protection and another without ESD protection were designed and implemented in 0.13 µm RFCMOS technology.The operation frequency of the LNA was 10 GHz.The ESD protection used encompasses PI topology ESD protection, comprising the primary ESD protection diodes, LNA gate inductor, secondary ESD protection diodes, and power clamps.The desired level of ESD protection for the LNA was 2000 V for the Human Body Model (HBM).The study was limited to the verification of the degradation of the S-parameters, noise figure, and ESD protection level at the LNA input.Comparing the simulated results of the LNA without ESD protection with the LNA with ESD protection, the only significant RF parameter degradation was observed iii in the noise figure (NF).The LNA without ESD protection exhibited NF=2.4 dB, while the LNA with ESD protection exhibited NF=3.4 dB.The LNA with ESD protection passed a 2000V ESD stress without showing leakage and degradation of the S-parameters and noise.The main contribution of this work is to show that the degradation of RF parameters can be minimized by choosing the appropriate ESD protection and by taking it into account in early stages of the design process.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".