Browsing by Author "Tseng, Neng-Chun"

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Exposure assessment of process by-product nanoparticles released during the preventive maintenance of semiconductor fabrication facilities
(2018-07-20)
Liao, Bo-Xi
;
Tseng, Neng-Chun
;
Li, Ziyi
;
Liu, Yingshu
;
Chen, Jen-Kun
This study characterized the process by product particles (mostly nanoparticles) released during the preventive maintenance of semiconductor fabrication facilities, such as chemical mechanical planarization (CMP), plasma-enhanced chemical vapor deposition (PECVD), and ion implantation. Manual sampling and real-time measurements with direct reading instruments were conducted to assess the exposure levels of nanoparticles and their physical and chemical properties. Significant amount of nanoparticles were observed in the breathing zone of the workers during the maintenance of the PECVD and ion implanters with the peak number concentrations as high as 6,470,000 and 65,444 #/cm(3), respectively, indicating that the deposited residual chemicals in the reaction chambers were released as airborne nanoparticles by the maintenance activities. In contrast, nanoparticles released during the maintenance of the local scrubber, CMP, and replacing CMP slurry drums were insignificant. Causes of the particle release were discussed and suggestions were made to mitigate the nanoparticle release and reduce the exposure levels.
The accuracy of the aerosol particle mass analyzer for nanoparticle classification
(2018-01-01)
Liao, Bo-Xi
;
Tseng, Neng-Chun
;
Tsai, Chuen-Jinn
;
環境工程研究所
;
Institute of Environmental Engineering
The aerosol mass measurement method, DMA-APM, measures a lower mass as compared to the electrical mobility diameter-based particle mass for sub-50 nm nanoparticles. The extent of underestimation increases with decreasing nanoparticle diameter and can reach as much as 20-80% for different nanoparticles between 10-20 nm. To study this issue, the DMA-APM system was tested with traceable size standards (PSL and NanoSilica) and laboratory generated silver nanoparticles. It was found that the extent of mass underestimation depended on Brownian diffusion as well as the strength of the classifying forces, and the extent was quantified by a dimensionless parameter lambda(c,P), which is suggested to be higher than 40 to eliminate the mass underestimation for size standards. Further analysis also showed that the uncertainty in the particle density of test nanoparticles should be as low as possible to minimize the error in the judgment on the accuracy of the APM. Finally, the absolute accuracy of the APM at different lambda(c,P) was determined by the size standards, which could be used to correct for the mass underestimation for sub-50 nm nanoparticles.