journal article·2016-09

Real-time and online screening method for materials emitting volatile organic compounds

Changhyuk Kim, Yong Tae Sul, David Y. H. Pui

Journal of Nanoparticle Research· Volume 18· Issue 9
Publisher page

Abstract

In the semiconductor industry, volatile organic compounds (VOCs) in the cleanroom air work as airborne molecular contamination, which reduce the production yield of semiconductor chips by forming nanoparticles and haze on silicon wafers and photomasks under ultraviolet irradiation during photolithography processes. Even though VOCs in outdoor air are removed by gas filters, VOCs can be emitted from many kinds of materials used in cleanrooms, such as organic solvents and construction materials (e.g., adhesives, flame retardants and sealants), threatening the production of semiconductors. Therefore, finding new replacements that emit lower VOCs is now essential in the semiconductor industry. In this study, we developed a real-time and online method to screen materials for developing the replacements by converting VOCs into nanoparticles under soft X-ray irradiation. This screening method was applied to measure VOCs emitted from different kinds of organic solvents and adhesives. Our results showed good repeatability and high sensitivity for VOCs, which come from aromatic compounds, some alcohols and all tested adhesives (Super glue and cleanroom-use adhesives). In addition, the overall trend of measured VOCs from cleanroom-use adhesives was well matched with those measured by a commercial thermal desorption–gas chromatography–mass spectrometry, which is a widely used off-line method for analyzing VOCs. Based on the results, this screening method can help accelerate the developing process for reducing VOCs in cleanrooms.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Chemical Sensor Technologies · Gas Sensing Nanomaterials and Sensors · Indoor Air Quality and Microbial Exposure

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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