Prognostic Detection of Electromigration Void Failure in Buried Metal Interconnect using Piezoresistive Sensors
Bibliographic record
Abstract
A novel applied reliability tool is presented to prognostically detect electromigation (EM) failures within VLSI buried metal interconnect trees. This is achieved non-destructively, in-situ, using piezo-restive sensors located at the likely point of failure. In practical applications, electrical detection of impending failures is limited to detecting the void growth at the onset of failure, when void growth almost entirely spans the metal segment. Prior to failure, the electrical changes are too small in magnitude to be pragmatically measured. Instead, piezo-resistive sensors are used to detect mechanical stress changes near the substrate as the failure progresses. A test chip has been developed in a cost effective 180nm technology with 24 active reservoir structures and respectively 24 piezo-restive sensors located at the segment's cathode where the void formation is stimulated. The metal segments are tuned to fail at an accelerated rate and include embedded mechanical sensors below to detect the changes. First a metal segment is tuned to fail by employing active reservoirs, extra current carrying metal extrusion that can alter the hydrostatic stress within metal lines proportionally to the applied current density, to accelerate void formation and subsequent growth. These allow geometrically identical metal segments to fail at different rates by tuning their respective current densities. Additionally, the accelerated void growth can be achieved without the need for elevated temperatures above 125C allowing for use in conventional devices and commercial applications. The local mechanical changes caused by void growth can be detected by taking advantage of the piezo-resistive properties of the semiconductor layers below the metal lines. To verify and monitor the test segment's resistance increase over time, four-wire resistance probes are built into the test structures. In this case the stress is measured and compared at two reference temperatures for two geometrically identical segments. The first segment with the active reservoir engaged as a sink and the other dormant as a healthy reference structure. The thermal expansion of a voided line is sufficiently different from the healthy reference and the void growth can be detected in this way.
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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".