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Putting Filtration to Work for Photoresist Contamination Control

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Putting Filtration to Work for Photoresist Contamination Control

If you asked a semiconductor process engineer to name their biggest challenge when tackling the next technology node, they would likely tell you it is figuring out how to achieve high device yields. This is mainly due to an increase in possible points of contamination as the number of potential contaminants grows and their sizes shrink. It is becoming particularly difficult to detect metal contaminants and pinpoint their root cause so they can be eliminated. That’s because they can form anywhere in the process flow.

EUV: Process Efficiency, New Defect Potential

As far as photolithography is concerned, the industry is transitioning from traditional immersion processes to extreme ultraviolet (EUV) lithography. While it is more efficient because it requires fewer process steps, EUV lithography also comes with process constraints that limit the number of photons per energy dose. This is causing an exponential increase in observable defects in sub-7nm node devices.

Researchers have explored ways to adapt photoresists to address the cause of these defects, only to create more opportunities for other types of defects to occur. For example, one novel photoresist formulation that uses acrylate and phenol copolymers to address stochastic issues in EUV lithography has itself become a source of microbridge defects. Additionally, other formulations result in various trace metal contamination that influences device performance and impacts yield.

Metallic Contamination Defies Single Solution

In general, metallic contaminants pose the biggest risk to circuit performance and yield. In fact, the interactions of metal contaminants in photochemicals are so complex that a single purification solution is neither available nor possible. Different methods must be designed to address each contamination type.

Current contaminant-removal techniques include electrodialysis, liquid-liquid extraction, metal organic frameworks, and ion exchange adsorbents. However, all of these were originally developed to purify wastewater or brackish water. They were not designed to handle the complexities of photochemicals. Among these methods, only ion exchange media have proven effective for purifying photochemicals.

Optimal Filtration and Purification Methods

Porous polymer resin columns and modified filtration membranes represent the two kinds of ion exchange media that have been successfully used in photochemical purification. Because a low flow rate is critical to the successful extraction of unwanted metal ions, the packed-bed-of-resin approach is both labor and time intensive. Alternatively, filtration membranes offer faster setup time, flexible processing, and easy handling.

In some photochemical filtration applications, microporous ultra-high-molecular-weight polyethylene filters are widely used because of their chemical inertness, cleanliness, and ability to be modified. For solvents, they have proven effective for extracting metal contaminants. When they are used to purify photoresist formulations, however, the interaction between the other components in the resist formulation and membrane surface functional groups creates a negative impact on photoresist performance and metal removal efficiency.

Fast-Tracking Filtration Research

The bottom line is that while chemical suppliers continue to make significant efforts to improve photoresist formulations that address defectivity challenges, this cannot be accomplished without simultaneous advancements and innovations in filtration technologies. Unfortunately, there is still work to be done to bring filtration solutions where they need to be to improve advanced technology node device yields.

At Entegris, we’ve put adsorption filtration research on the fast track to come up with solutions that can be adapted to address the ever-changing world of metal contamination removal. We’re going beyond traditional experimentation to implement computational methods using artificial intelligence (AI) and machine learning (ML) in our development cycle to help us tailor filtration membranes to selectively address contaminants.

We believe that filters of the future must address complex contamination challenges across all semiconductor manufacturing processes. These can only be created by combining deep technical knowledge, collaborations with the semiconductor value chain, and advanced computational resources.

To learn more about the work Entegris is doing to innovate filtration solutions to address challenges in photochemical purification and more, read the article “Modifying Polymer Surfaces for Contamination Control: Challenges and Opportunities for Improving Device Yield” in our 2022 Scientific Report.

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