Intrinsic thin film stresses in multilayered imaging pixels
Bibliographic record
Abstract
When thin film devices are integrated in display or imaging arrays, the intrinsic mechanical stresses in the films can be severe enough to cause the films to either crack or even peel off the underlying substrate layer. This is a particular concern in large area x-ray imaging applications based on molybdenum/amorphous silicon (Mo/a-Si:H) Schottky diodes. This family of image sensors can be used for direct detection of x rays without the need for a phosphor layer [W. Zhao and J. A. Rowlands, Med. Phys. 22, 1595 (1995)]. In these sensors, the interaction of x-ray photons with Mo leads to injections of high energy electrons into a reverse-biased a-Si:H depletion layer, thereby producing an amplified output signal due to charge multiplication. The generated charge is read out by a thin film transistor (TFT) which is composed of intrinsic amorphous silicon (i-a-Si:H) as the active channel layer, silicon nitride (a-SiNx) as the insulating layer, and highly doped microcrystalline silicon (n+μc-Si:H) as the contact layer. For example, the i-a-Si:H layer is in compressive stress with a magnitude of 0.116 GPa, whereas low temperature, low resistivity n+μc-Si:H layer is in compressive stress of 1.47 GPa. The a-SiNx layer is in tensile stress of 0.222 GPa. The heavy metals used in the x-ray detector, such as Mo and chromium (Cr) (deposited at 5 mTorr), are found to be in tensile stress of 0.807 and 0.575 GPa, respectively. This article presents the internal mechanical stresses in thin film devices used in x-ray imaging, and more importantly, how the total film stress can be reduced without undermining device performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".