The reliability of packaged laser diodes is heavily dependent on the quality of the die attach. Even a small void or delamination may result in a sudden increase in junction temperature, eventually leading to failure of the operation. The contact thermal resistance at the interface between the die attach and the heat sink plays a critical role in thermal management of high-power laser diode packages. This paper focuses on the investigation of interface contact thermal resistance of the die attach using thermal transient analysis. The structure function of the heat flow path in the T3ster thermal resistance testing experiment is utilized. By analyzing the structure function of the transient thermal characteristics, it was determined that interface thermal resistance between the chip and solder was 0.38 K/W, while the resistance between solder and heat sink was 0.36 K/W. The simulation and measurement results showed excellent agreement, indicating that it is possible to accurately predict the interface contact area of the die attach in the F-mount packaged single emitter laser diode. Additionally, the proportion of interface contact thermal resistance in the total package thermal resistance can be used to evaluate the quality of the die attach.

We report a single-wavelength tunable mode-locked fiber laser. The single wavelength can be tuned from 1537.49 nm to 1608.06 nm by introducing a Sagnac loop filter. As far as we know, this is the widest single-wavelength tuning range achieved in an erbium-doped mode-locked all-fiber laser based on nonlinear amplifying loop mirror (NALM). The laser’s pulse width changes from 549 fs to 808 fs throughout the tuning process, the maximum average output power is 5.72 mW, and the single-pulse energy is 0.34 nJ at a central wavelength of 1556.53 nm. This laser source can serve as an efficient tool for applications that require a broad tunability range. The combination of femtosecond pulses and extensive wavelength tuning capabilities makes this laser system highly valuable in fields such as fiber optic communications, spectroscopy, sensing, and other applications that benefit from ultrafast and tunable laser sources.

Raman spectroscopy is widely used for material identification, but mixture analysis remains challenging due to noise and fluorescence interference. To tackle this issue, we propose the Multiplet Network, an advanced deep-learning model specifically designed for identifying components in mixtures. This model employs a shared-weight residual network to map both mixture and candidate spectra into a unified feature space, where least-squares regression is utilized to predict the components. Our framework enhances feature extraction and component identification capabilities, outperforming traditional regression methods. Experimental evaluations on the RRUFF dataset showed that our model achieved superior accuracy, especially as the number of candidate spectra increased. Furthermore, it exhibited remarkable robustness against Gaussian noise and baseline variations, maintaining high accuracy under challenging conditions. To assess the real-world applicability, the model was tested on experimentally collected mixture spectra with significant noise and baseline shifts. The results confirmed that it effectively identified major components under complex spectral conditions. Additionally, the unique structure of the model’s feature extraction combined with least squares allowed it to handle varying sizes of spectral libraries, ensuring both flexibility and scalability. Overall, our approach provides a robust and adaptable solution for Raman mixture analysis, with strong potential for complex chemical and material identification in practical applications.