MétaCan
Menu
Back to cohort
Record W3191072511 · doi:10.1109/ectc32696.2021.00034

Direct Bonded Heterogeneous Integration (DBHi) Si Bridge

2021· article· en· W3191072511 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsInterconnectionSystem in packageIntegrated circuit packagingThermocompression bondingReliability (semiconductor)ChipThree-dimensional integrated circuitComputer scienceWire bondingMaterials sciencePackage on packageElectronic packagingJoinsQuad Flat No-leads packageMechanical engineeringElectronic engineeringEngineeringAdhesiveLayer (electronics)Power (physics)Composite material

Abstract

fetched live from OpenAlex

We introduce a new packaging technology termed as Direct Bonded Heterogeneous Integration (DBHi) where a Si-bridge is directly bonded to and in between processor chips using Cu pillars, allowing high-bandwidth low-latency low-power communication between the chips. The DBHi package structure, test vehicle design, and bond and assembly details are first described. The test vehicle package consists of chips with standard interconnect pitch where they join to a laminate chip-carrier and fine-pitch pads in the region where the chips joins to a bridge. The bridge has Cu pillars correspondingly mating to the pads on the chips. The bond and assembly sequence starts with first joining the silicon chips and bridge using a thermocompression bonding process followed by a mass reflow join of the chips to the laminate. The assembly is then underfilled and capped using specialized techniques. Mechanical modeling was extensively used to simulate the DBHi structure and assembly process to allow material selection and reliability prediction. The mechanical models were calibrated using warpage measurements. The stress/strain reliability metrics of the DBHi package are compared to a non-bridge package of the same dimensions. Results show that the main focus should be directed towards ensuring a robust assembly process as the standard reliability stress/strain metrics of the DBHi package are very similar to a non-bridge package. Thermal measurements using chip heaters and temperature sensors were conducted to calibrate a numerical thermal model of the DBHi package. The thermal model was exercised to show the relation between the allowable chip and bridge power densities for the particular package size and cooling conditions. DBHi test packages were created using the best-known assembly process and then measured for continuity performance. A variety of inter- and intra-bridge daisy chain nets were incorporated into the test vehicle for continuity measurements. Post-assembly continuity measurements demonstrated a robust assembly process for multiple rounds of assembly. Reliability performance was demonstrated using standard JEDEC tests of thermal cycling, aging, and temperature/humidity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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

Quick stats

Citations44
Published2021
Admission routes1
Has abstractyes

Explore more

Same topic3D IC and TSV technologiesFrench-language works237,207