The intensely manual practices of cleanroom classification and routine monitoring are time-consuming and prone to data errors. Particle counter performance and accuracy can vary widely, leading to inaccurate particle counting data.
Cleanrooms may be considered as a system, with people typically the largest source of airborne particulate contamination and the cleanroom’s air handling system designed to remove this contamination. The balance between incoming contamination and the removal of contamination is designed to deliver the required air quality of the cleanroom ‘system’ to meet the requirements of the processes.
Routine monitoring data can be trended to detect any deviations or tendency to drift towards an out-of-specification situation (OOS) that may threaten product quality. However, if the data integrity cannot be relied upon, either through frequent manual data transcription errors, or through poor particle counter-to-counter measurement issues, then the benefit of data trending can be called into question.