## 1 Introduction  
Airborne molecular contamination (AMC) represents a wide range of gaseous contaminants in the cleanroom air environment at part-per-billion or -trillion (ppb or ppt) levels [1, 2]. When AMC is exposed to ultraviolet light sources of lithography tools (e.g., extreme ultraviolet lithography, EUVL), it can form particle and haze contamination on wafers and photomasks during semiconductor manufacturing processes, thus increasing defects of the products [3, 4, 5, 6]. Therefore, detecting and controlling AMC have been big issues for enhancing the product yield in the semiconductor industry. However, it is difficult to detect due to its extremely low concentration of AMC in the cleanroom air [7, 8]. Even though gaseous contaminants generally have been captured using porous media [9, 10, 11], such as activated carbons, through adsorption, it is difficult to remove AMC totally from the air, and the unfiltered AMC can cause the secondary contamination problems (e.g., particles or haze) in the semiconductor manufacturing processes [12]. In addition, different physicochemical properties of various AMC require multiple gas filter layers for different kinds of AMC, which increases maintenance cost for the semiconductor manufacturing.  
Conventional methods for quantifying AMC in the cleanroom air are mostly conducted by sampling AMC in the cleanroom air and analyzing the samples at off-site laboratories using precise instruments [1; 13; 14; 15]. Due to the ultra-low concentration of AMC, certain sampling-time periods (e.g., hours to days) are normally required to get enough AMC concentration higher than the lower detection limits of the analysis instruments. In this case, we may lose the critical time for taking care of AMC in emergency cases due to the time difference between the sampling and analysis. In other words, the analyzed data show average values in the past, which cannot represent real-time data. This makes it difficult to find the locations where AMC is dominantly originated or the AMC concentration is higher than surrounding (called as hot spots) in the cleanroom immediately. If a portable method for determining the concentration of AMC in the cleanroom air in real-time is available, the AMC problems would be mitigated substantially. AMCs in the cleanroom should be more than a species such as sulfate, nitrate, ammonia, condensable organics, dopants, etc. For analyzing the chemical compositions of AMCs, expensive instruments are required. Although the instruments, such as proton transfer reaction-mass spectroscopy (PTR-MS) [16; 17] for organic AMCs, are ready in the cleanroom for monitoring, it is difficult to move them and detect hot spots in real-time.  
[PERSON] and his colleagues have developed a detection method for AMC, which deploys a gas-to-particle conversion under the soft X-ray irradiation (soft X-ray radiolysis) and tested its feasibility to detect the ultra-low gaseous air pollutants in the lab. Then, the method was applied to different kinds of AMC-related issues, including evaluating the gas filtration performance of granular activated carbons [7; 8; 11]. Although the method has been deployed in the previous laboratory experiments with fixed positions, it can be used to determine the real-time AMC concentration in the cleanroom as well by configuring a portable system using the method, because the method was originally designed to detect AMC hot spots in the cleanroom in real-time, which was difficult to conduct by conventional techniques before.  
In this study, we investigated AMC levels in the cleanroom air environment deploying a portable soft X-ray radiolysis detector, which consists of a soft X-ray radiolysis chamber and a scanning mobility particle size (SMPS, a combination of a nano differential mobility analyzer (nano DMA) and an ultra- condensation particle sizeer (UCCP), Model 3936N76, TSI Inc., Minneapolis, MN, USA). The measurements were conducted at different sub-locations in a cleanroom at the University of Minnesota, MN, USA.