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EOTPR Fine Pitch Probing for Die-to-Die Interconnect Failure Analysis

2025· article· W4415990515 on OpenAlex
Bernice Zee, Wen Qiu, Aaron Wai Ken Lee, Jesse Alton, Thomas P. White, David D. Kim, Martin Igarashi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings - International Symposium for Testing and Failure Analysis · 2025
Typearticle
Language
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsAdvanced Micro Devices (Canada)
FundersAdvanced Micro Devices
KeywordsInterconnectionRoot causeGranularityFault detection and isolationIsolation (microbiology)TRACE (psycholinguistics)Integrated circuit packagingCatastrophic failureFault (geology)

Abstract

fetched live from OpenAlex

Abstract Debug and physical failure analysis (PFA) of heterogeneously integrated semiconductor packages, particularly die-to-die (D2D) input/output (I/O) type fails, has become very challenging due to the lack of direct access to the I/Os from the package substrate to do static open/shorts fault isolation and limited test program granularity to determine which location along the D2D interconnect trace is failing. Thus, a suitable electrical fault isolation technique is required to ensure high success rate for root cause analysis. This paper discusses how EOTPR is used to isolate defects on a D2D interconnect trace of a chiplet advanced packaging using local silicon bridge with reasonable accuracy. Minimal sample preparation was needed to expose the I/O bumps for probing, thus minimizing the risk of artifacts that may cause the defect to be lost. A case study will demonstrate the successful application of the technique.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.009
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.248
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it