Low-Temperature Bonding for Silicon-Based Micro-Optical Systems
Bibliographic record
Abstract
Silicon-based integrated systems are actively pursued for sensing and imaging applications. A major challenge to realize highly sensitive systems is the integration of electronic, optical, mechanical and fluidic, all on a common platform. Further, the interface quality between the tiny optoelectronic structures and the substrate for alignment and coupling of the signals significantly impacts the system’s performance. These systems also have to be low-cost, densely integrated and compatible with current and future mainstream technologies for electronic-photonic integration. To address these issues, proper selection of the fabrication, integration and assembly technologies is needed. In this paper, wafer level bonding with advanced features such as surface activation and passive alignment for vertical electrical interconnections are identified as candidate technologies to integrate different electronics, optical and photonic components. Surface activated bonding, superior to other assembly methods, enables low-temperature nanoscaled component integration with high alignment accuracy, low electrical loss and high transparency of the interface. These features are preferred for the hybrid integration of silicon-based micro-opto-electronic systems. In future, new materials and assembly technologies may emerge to enhance the performance of these micro systems and reduce their cost. The article is a detailed review of bonding techniques for electronic, optical and photonic components in silicon-based systems.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".