High-speed contactless sintering characterization for printed electronics by frequency-domain thermoreflectance
Bibliographic record
Abstract
Abstract Printed electronics is an alternative manufacturing paradigm for low-cost and large-area microelectronic devices and systems. Metal nanoparticle (MNP) inks are favorable to print conductors due to their high electrical conductivity. As-printed MNP ink requires sintering to become electrically conductive. High-quality MNP conductors require monitoring and optimization of the sintering process. Traditionally, electrical conductivity is measured to monitor the different sintering stages. This requires destructive probing or fabrication of dedicated test structures, which is challenging for in-line monitoring of high-volume manufacturing. Here, we demonstrate that frequency-domain thermoreflectance (FDTR), an optical pump-probe technique, can be used for process monitoring. Conductive features are inkjet printed with a silver nanoparticle ink. Intense pulsed light (IPL) sintering is used rather than traditional thermal sintering due to its capability of millisecond sintering. Thermal conductivity of IPL sintered features is measured using FDTR, where a frequency-modulated heat flux is applied with a pump laser and the obtained thermal phase of the probe laser is fitted to a thermal model. Thermal conductivity measured from FDTR agrees well with thermal conductivity calculated using Wiedemann–Franz Law from electrical conductivity measurements. By appropriately choosing six FDTR pump frequencies with the highest sensitivity and taking all the selected frequency-vs-phase data points at once, we can measure thermal conductivity in 12 s, a fraction of the traditional measurement time. In this way, the measurement time decreases considerably, and thermoreflectance becomes a suitable characterization technique for high-throughput manufacturing. A Monte Carlo-based prediction was performed to observe the effect of shorter measurement time on phase noise, and a much faster measurement configuration is proposed with an acceptable uncertainty in measurement. Our results demonstrate a simple approach for high-speed non-contact characterization of metal nanoparticle conductors with the combination of high-speed printing and high-speed sintering for low-cost electronics manufacturing.
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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 | 0.001 |
| 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) | 0.002 | 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".