(Invited) Hermetic Wafer-Level Packaging of Microbolometers for Uncooled Thermal Cameras
Bibliographic record
Abstract
Infrared (IR) and thermal imaging industry has been increasingly expanding outside of its traditional military scope to cover a variety of consumer products, surveillance, and security applications. The market for thermal imaging is expected to double in the next 5 years and reach an amount of $8 billion by 2021. This increasing demand for thermal imaging technology is expected to reduce the unit price per device, which puts pressure on the industry to lower production costs. For low-cost consumer products, thermal imaging is based on microbolometer technology which does not require cooling, works in short-range, and provides live-video feed of surroundings. Microbolometers comprise of IR sensitive material that undergoes a change in electrical resistance upon absorbing IR radiation. In order to minimize losses, improve the resolution of the thermal imager, and extend its lifetime, the material is thermally isolated from its surrounding environment by enclosing it in a low vacuum micro-cavity. The micro-cavity is formed by bonding a cap wafer to the device wafer, which allows encapsulating tens of devices at the same time. This form of bonding, known as wafer-level packaging, significantly cuts the microbolometer production cost. When used for consumer products, the thermal imaging device must operate for 5-10 years, depending on the targeted application, before its performance deteriorates. To prolong the lifetime of the device, the vacuum in micro-cavities must be sustained. Several steps must be implemented in order to achieve and maintain proper operation conditions: i) a hermetic sealing of the cavity should be realized to minimize gas leakage from surrounding environment; ii) degassing of the system before seal-off is needed to dislodge and release trapped gases within the materials; and if necessary, iii) incorporating a non-evaporable getter which acts as a chemical pump to rid the system of residual gases during the device lifetime. In this presentation, we provide an overview of current challenges in hermetic wafer-level packaging of microbolometers, which is a crucial step in the large scale production of uncooled thermal cameras. The presentation will address advantages and limitations of different bonding processes. Special focus will be made on fluxless AuSn solder and its use to achieve hermetic wafer-level packaging. Results of temperature-dependent surface analysis studies including in situ and ex situ spectroscopic and microscopic techniques (LEEM/PEEM, XPS, SEM-NanoAuger, and TEM) will be discussed to highlight key mechanisms in wafer preprocessing (e.g. patterning of the seal rings) and establish key parameters controlling the quality of the bonding. The role of pre-processing at elevated temperatures in degassing the cavity from non-getterable gases and its influence on Sn and Ti surface diffusion will be discussed. This surface diffusion yields to the formation surface oxide, which is detrimental to the bonding process.
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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 | 0.002 | 0.001 |
| 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".