UC Davis

A bonding process was developed for glass-to-glass fusion bonding using Borofloat 33 wafersresulting in high bonding yield and high flexural strength. The Borofloat 33 wafers went through a two-step process with a pre-bond and high-temperature bond in a furnace. The pre-bond process included surface activation bonding using O2 plasma and N2 microwave radical activation where the glass wafers were brought into contact in a vacuum environment in an EVG 501 Wafer Bonder. The optimal hold time in the EVG 501 Wafer bonder was investigated and concluded to be a 3 h hold time. The bonding parameters in the furnace were investigated for hold time, applied force, and high bonding temperature. It was concluded that the optimal parameters for glass-to-glass Borofloat 33 wafer bonding were at 550 °C with a hold time of 1 h with 550 N of applied force. From the bonding process, we have developed a statistical model approach the quality analysis (QA) and quality control (QC) of a micro preconcentrator chip (μPC) when manufactured at scale for chemical and biochemical analysis. To test the proposed model, a medium-sized production of 118 chips were batch manufactured at the wafer level and subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process leading up to the final chip, with some of the highlights being XY etch accuracy of ± 0.089 μm, etch depth accuracy of ± 0.645 μm, bond charge accuracy of ± 4.9 mC, electrode fabrication accuracy of ± 3.1 Ω, and thin film silicon oxide of 7.24 nm. We implemented a principal component analysis (PCA) model to score individual chip performance, and we observed the first 2 principal components represents 74.28% of chemical testing variance with 111 of 118 chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing steps. In our analysis, we find the amount of sorbent mass present in the chip (VIS = 2.6) and heater and RTD resistance values (VIS = 1.1) the manufacturing parameters with the most impact on chip performance. While these were not surprising, other non-obvious latent manufacturing parameters did have quantified influence. Statistical distributions for each manufacturing step will allow future large scale production runs to be statistically sampled during production itself to perform QA/QC in a real-time environment.