Metrology and Characterization of Impurity Transport During Cleaning of Micro and Nano Structures

Abstract

A major challenge in the manufacturing of micro and nano devices is the cleaning, rinsing, and drying of very small structures. Without a technology for in situ and real-time monitoring and controlling, the rinse processes that account for a significant fraction of the total processing steps use a large amount of water and energy perhaps unnecessarily. This "blind" processing approach leads to waste that can have significant economic and environmental impacts. An electrochemical residue sensor (ECRS) has been developed and is aimed at in situ and real-time measurement of residual contamination inside the micro and nano structures. Using this technology, the mechanisms and bottlenecks of cleaning, rinsing, and drying can be investigated and the processes can be monitored and controlled.An equivalent circuit model was developed to assist the design of the sensor; its validity was proved by the first prototype. The simulation results and the experimental data predicted a good sensitivity in a wide range of operational frequency. To use the sensor in a practical rinse tank setup, the sensor-on-wafer prototype was designed and fabricated. Both the fab-scale and the lab-scale tests were performed and results illustrated many successes. The sensor is the first and the only available technology that provides the in situ and real-time cleanness information in the microstructures during the rinse processes. The sensor results distinguished four different types of rinse processes and showed high sensitivity to the ionic concentration change in the microstructures. The impacts of cleaning and rinsing parameters such as flow rate, temperature, cleaning solution concentrations, and process time on the sulfuric acid rinsing efficiency were investigated by using the sensor. The investigation discovered that sulfuric acid rinsing is a two-stage process: a flow-control stage and a desorption-control stage. A comprehensive rinse model was developed to correlate the transport process and the trench impedance that is the sensor's signal. This model combined with the experimental data proved that increasing flow rate in the overflow rinse has a low efficiency for the rinse processes controlled by the surface reactions. The model, for the first time, shows the dynamics of the charging of the silicon dioxide surface and the dynamics of the potential build-up in the solution. It also discovered that the cation rinsing is a challenge if the cation adsorbs on or reacts with the surface.