A Correlative Microscopic Workflow For Nanoscale Failure Analysis and Characterization of Advanced Electronics Packages
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.
Bibliographic record
Abstract
Abstract Microscopic imaging and characterization of semiconductor devices and material properties often begin with a sample preparation step. A variety of sample preparation methods such as mechanical lapping and broad ion beam (BIB) milling have been widely used in physical failure analysis (FPA) workflows, allowing internal defects to be analyzed with high-resolution scanning electron microscopy (SEM). However, these traditional methods become less effective for more complicated semiconductor devices, because the cross-sectioning accuracy and reliability do not satisfy the need to inspect nanometer scale structures. Recent trends on multi-chip stacking and heterogenous integration exacerbate the ineffectiveness. Additionally, the surface prepared by these methods are not sufficient for high-resolution imaging, often resulting in distorted sample information. In this work, we report a novel correlative workflow to improve the cross-sectioning accuracy and generate distortion-free surface for SEM analysis. Several semiconductor samples were imaged with 3D X-ray microscopy (XRM) in a non-destructive manner, yielding volumetric data for users to visualize and navigate at submicron accuracy in three dimensions. With the XRM data to serve as 3D maps of true package structures, the possibility to miss or destroy the fault regions is largely eliminated in PFA workflows. In addition to the correlative workflow, we will also demonstrate a proprietary micromachining process which is capable of preparing deformation-free surfaces for SEM analysis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it