Physical and Electrical Performance Comparison of Identical 28 nm Qualcomm Telecommunication Die Produced by Samsung and TSMC
Bibliographic record
Abstract
Abstract In today’s competitive semiconductor environment, product performance and market timing has never been more valuable. Design IP, speed to market, and taking advantage of the most advanced technology are three ways fabless companies can maintain an advantage over the competition. Foundries target these demands by offering superior support, competitive technology, and rapid development cycles. Using the advanced tool suites of SEM, FIB, TEM, and Atomic Force NanoProbing (AFP) the failure analysis community now has the ability to investigate and compare foundry performance on the device level. The 28 nm LP Qualcomm “SHELBY” die is dual-sourced from both Samsung and TSMC, and is the primary die in the MDM9215 4G/LTE modem used in several smartphones. This represents a unique case of leading technology, available to the public, to qualify for electrical performance on the device level using the AFP and the corresponding physical differences using SEM and TEM. These advanced FA techniques were employed and were able to identify manufacturing differences between foundries. They were then used to relate the physical variations with the electrical device performance. The HG11-N3877 fabricated by TSMC and the HG11-N9204 fabricated by Samsung were the subjects of this comparison (see Error! Reference source not found.). The investigation located spatial and geometric variations of the SRAM devices using cross sectioning and TEM imaging. This was followed by Electrical Characterization of multiple SRAM Cells using the AFP. The electrical measurements showed clear differences in device parameters. These differences highlight manufacturing process differences between the two companies that could directly relate to chip performance.
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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 | 0.001 |
| 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".