SPIN DOCTORING: A Multiple Regression Analysis of the Efficacy of Local Exhaust Ventilation in Reducing Solvent Exposures During Spin-Coat Operations

SPIN DOCTORING: A Multiple Regression Analysis of the Efficacy of Local Exhaust Ventilation in Reducing Solvent Exposures During Spin-Coat Operations 
Ben Kollmeyer – Univ of California at Berkeley (SSA Journal Volume 11 Number 1 – Spring 1997 pp. 35 – 46 )

A multiple regression model is used to assess the influence of local exhaust ventilation and other variables on worker solvent exposure during spin-coating operations. The data used in the model were collected as part of the Semiconductor Health Study (Dr. Marc Schenker et. Al.), and consists of exposure data for the solvents EGEEA, PGMEA, EGME, Xylene, and n-BA. The principles behind multiple regression modeling are discussed including assumptions, limitations, and how to interpret outputs. Based on the 73 data sets obtained from 24 fabs across 10 companies, the analyses conclude that the variables exerting a significant effect on exposure are the number of times the spin-coater is loaded and unloaded, the percentage of solvent in the applied mix, and the number of fresh air changes in the work area. Local exhaust ventilation on the solvent supply basin is found to be more effective than on the solvent waste basin, while ventilation on the bake plate appears ineffectual. Overall, the variables considered account for 25% of the variability observed, indication that exposure is being dictated to a great extent by factors not included in the analysis.