Impedance Characterization of Power Delivery Network in a Flip Chip Package on a Printed Circuit Board
Bibliographic record
Abstract
This paper discusses low impedance characterization techniques used for power delivery network (PDN) in today's high-performance digital systems. More importantly, sensitivity analysis is presented to study impact of measurement accuracy against landing locations of probes onto a flip chip package and more specifically relative distance between two micro-probes in S21 measurements by vector network analyzer (VNA). Milliohms impedances across wide bandwidth, from approximately DC to GHz frequency range, can be measured accurately and well-correlated with simulation data on a microprocessor micro PGA substrate sitting in a socket mounted onto a printed circuit board (PCB) with cutting edge design of capacitive decoupling scheme. Conventionally, transfer-impedance obtained by two-port VNA measurement is used for PDN characterization. Being challenged by accessibility of test points in a complex package and board design, closely landing of the two micro-probes onto C4 pads would induce unwanted measurement noise caused by inductive coupling while excessive distance between the two micro-probes would result into artificial low impedance caused by parasitic inductance in the power planes of the package. Cautious arrangement of probing scheme in measurement that mimic to port setting in simulation is crucial in low impedance characterization of PDN at frequency range of interested. The accuracy of this low impedance characterization with optimized probing locations is verified by analytic calculation and simulation.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".