Fabrication of Precise Fluidic Structures in LTCC
Bibliographic record
Abstract
A number of emerging applications of low‐temperature co‐fired ceramic (LTCC) require embedded fluidic structure within the co‐fired ceramic and or precise external dimensional tolerances. These structures enable the control of fluids for cooling, sensing, and biomedical applications, and variations in their geometry from the design can have a significant impact on the overall performance of the devices. One example of this type of application is a multilayer cooler developed recently by the authors for cooling laser diode bars. In many laser systems, laser diodes are the primary emitters, or assemblies of these diode bars are used to pump traditional laser crystals such as Nd:YLF. Assemblies of these diodes require large amounts of electrical current for proper operation, and the device operating temperature must be carefully controlled in order to avoid a shift in the output wavelength. These diodes are packaged into water‐cooled assemblies and by their nature dissipate enormous amounts of heat, with waste heat fluxes on the order of 2000 W/cm 2 . The traditional solution to this problem has been the development of copper multilayer coolers. Assemblies of laser diodes are then formed by stacking these diode bars and coolers. Several problems exist with this approach including the erosion of the copper coolers by the coolant, a requirement for the use of deionized water within the system, and a significant CTE mismatch between the diode bar and the metal cooler. Diodes are bonded to these metal structures and liquid coolant is circulated through the metal layers in order to cool the diode bar. In contrast, the coolers developed by the authors utilize fluid channels and jets formed within LTCC as well as embedded cavity structures to control the flow of a high‐velocity liquid and actively cool the laser diode bars mounted on the surface of the LTCC. The dimensional tolerances of these cooler assemblies and complex shapes that are used to control the fluid can have a significant impact on the overall performance of the laser system. This paper describes the fabrication process used to create the precise channel and jet structures used in these LTCC‐based coolers, as well as some of the challenges associated with these processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".