Package-Level Evaluation System for Power Integrity Study
Bibliographic record
Abstract
Abstract The constant evolution and scaling-up of applications like AI have been putting an ever-increasing demand for higher speed and performance on semiconductor chips. Consequently, power consumption by semiconductor chips has been on the rise in the recent past. FPGAs, GPUs and AI processors can easily consume thousands of watts of power. Proper power delivery is critical for the operation of these power-hungry chips. Inadequate power delivery can limit the functionality and performance of the chips, result in heating, lower lifetime of the chips and more. As a result, Power Integrity (PI) of chips and systems has emerged as a very important topic in industry. Considering the power delivery network (PDN) of chips and systems in detail is essential starting from the planning and architecture definition stages. The evaluation of PDN should continue through the development and deployment phases as well. Several CAD solutions exist for the modelling and simulation of power delivery into chips and systems. Simulations have to be complemented with actual silicon data-collection and analysis in order to produce optimal results. Not many hardware solutions are readily available for this purpose. This paper describes the architecture, implementation, details of operation, programming aspects and usage model of a customized silicon chip that will be used as a Power Integrity evaluation tool. The chip forms the heart of the PI evaluation system. The usage of the evaluation system in developing a Power Delivery Network model is covered. This chip has a simple implementation and is easy to program and use - yet has substantial PI evaluation capabilities. This evaluation system has a variety of applications including: • Power Delivery Network (PDN) study • Validation of PDN simulation models • On-chip and on-package decoupling capacitance evaluation • Micro-bump, C4-bump and BGA plan evaluation This highly configurable chip has the ability to do automated on-chip measurements through a test-friendly interface (simple, low pin-count & flexible) and has been tested on a simple and inexpensive test-platform. A large number of use-case scenarios were programmed, data was collected and analyzed from this evaluation system. Simulations were done using PDN models and correlated to above mentioned measurement data. It is concluded that this evaluation system is a valuable tool in the Power Integrity study of systems with semiconductor chip packages along with board (PCB).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.014 |
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".