ALD coatings have been developed to protect process equipment against aggressive process conditions, specifically fluoride plasma. Here alumina and yttria based ALD coatings were deposited in a large-batch ALD reactor on silicon samples and mock-up showerheads. High conformality in 1:20 aspect ratio features was demonstrated. Plasma etch testing of the film revealed high resistance against the etch process for all the films in the Al2O3-Y2O3 system. Y2O3 mixing into the Al2O3 film was shown to rapidly increase the etch resistance of the film with diminishing effect as pure yttria was approached. Etch damage on the film was uniform showing no preference on grain boundaries or other features.
As semiconductor devices become more complex, the amount of sensor data from manufacturing equipment also increases, resulting in an increase in the number of abnormal behavior cases not confirmed by existing methods. To address this, we use the weighted gene co-expression network analysis (WGCNA) method from the bioinformatics field for monitoring equipment health 1) constructing sensor co-expression networks, 2) identifying modules and their significance, and 3) detecting abnormal sensors using a graphical approach. This method led to a 0.3% increase in yield rate by improving the similarity between sensors and modifying the manufacturing process conditions.
Industrial semiconductor electrodeposition plating cells require recirculation of process chemical with consistent flow and minimal contaminants to prevent defects from developing during film deposition. This manuscript investigates how recirculation nozzle quality and poor nozzle machining can affect bath chemical uniformity. Computational fluid dynamics simulations are utilized to visualize bath chemical velocities based on variable nozzle conditions in four case studies. Results show that strict quality control of inlet nozzles, in conjunction with proper mounting angles, induce laminar bath flow. Greater fluid uniformity and laminar flow then translate to a reduction of in-line defects and increased wafer yield.
The wafer fabrication process in semiconductor manufacturing involves thousands of sequential steps from different process modules, such as Chemical Mechanical Planarization (CMP), Chemical Vapor Deposition (CVD), Wet/Dry Etching, etc. The high complexity of the processes entails the risk of a process failure and leads to a long manufacturing cycle time and higher greenhouse gas emissions (GHG) before the wafers eventually reach the probe module for electrical tests. This is particularly evident as 3D NAND devices have been introduced into the industry as the layers are stacked vertically and more process steps are involved in the flow compared to 2D NAND. Therefore, one of the main approaches to process improvement is process simplification by restructuring the process flow to allow for less complex processes or even the elimination of steps without compromising yield and quality. In this way, wafer fabrication and the manufacturing process can be made more efficient and environmentally friendly, which not only shortens the manufacturing cycle time but also reduces the total cost per wafer (CPW) or even eliminates the capital expenses required for tool purchase in high-volume manufacturing facilities. This paper elaborates on various process evaluation methodologies and techniques by understanding the root cause of defects that motivated CMP buff insertion and defect improvement methods through cleaner chemical vapor deposition processes, optimized wet cleaning or scrubbing, or improved CMP cleaning. Some examples of such process simplification work on replacement gate (RG) NAND are discussed in this paper, such as de-ionized water (DIW) Buff CMP elimination (by upstream step Oxidation Clean optimization), Ceria based Slurry Buff CMP elimination (by improving upstream deposition cleanliness improvement), and CMP buff step with new slurry and consumable configuration for lower cost and reduction of greenhouse gas emissions.
In a world of chip shortages and constrained supply, unplanned manufacturing downtime is extremely costly. Avoiding unplanned equipment downtime through predictive maintenance results in increased yield, increased throughput, and reduced costs. An advanced statistical method for the evaluation of time series sensor data is herein proposed for the purpose of predicting equipment failures in Chemical Vapor Deposition (CVD) systems. Through the simultaneous evaluation of all available sensor data, the algorithm develops an expected model of the total process' behavior. Deviation from expected behavior may be interpreted as pointing to elevated risk of equipment failure, or of a process upset that may result in product that falls outside of manufacturers’ specifications, leading to costly containment measures, customer returns, and other costs.Examples from recent Industrial Experiments in Wafer Epitaxy processes are shared to demonstrate the efficacy of the method.
