Formation and detection of subpellicle defects by exposure to DUV system illumination

Grenon, Brian J.; Peters, Charles R.; Bhattacharyya, Kaustuve; Volk, William Waters

doi:10.1117/12.373311

Cited by 16 publications

(8 citation statements)

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“…FTIR and Raman spectroscopy measurements have shown that ammonium sulfate comprises the highest content in the defect blend. [11][12] In the following sessions, we will discuss categories of commonly seen contamination defects to better understand the defects formation mechanism and how they evolve into printable defects depending upon the chemical and physical properties of the residing surface, along with how they behave with respect to Litho3 detector. For the purpose of this work, defects are categorized based on their location relative to the design features.…”

Section: Contamination Overviewmentioning

confidence: 99%

Automatic optimization of MEEF-driven defect disposition for contamination inspection challenges

et al. 2007

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Ever-tightened design rules and ensuing aggressive OPC features pose significant challenges for wafer fabs in the pursuit of compelling yield and productivity. The introduction of advanced reticles considerably augments the mask error enhancement factor (MEEF) where progressive defects or haze, induced by repeated laser exposure, continue to be a source of reticle degradation threatening device yield. High resolution reticle inspection now emerges as a rescue venue for wafer fabs to assure their photomask integrity during intensive deep UV exposure. Integrated in the high resolution reticle inspection, a MEEF-driven lithographic detector "Litho3" can be used run-time to group critical defects into a single bin. Previous investigations evinced that critical defects identified by such detector were directly correlated with defects printed on wafer, upon which fab users can make cogent decisions towards reticle disposition and cleaning therefore reduce cycle time.One of the challenges of implementing such detector resides in the lengthy set up of user-defined parameters, from practitioner standpoint, can considerably extend reticle inspection time and inevitably delay production. To overcome this, an automatic simulation program has been written to optimize Litho3 settings based off a pre-inspection in which only default Litho3 values are needed. Upon completion of the pre-inspection, the images are then scanned and processed to extract the optimal Litho3 parameters that are largely dependent upon the feature size characteristics and local MEEF. Thus optimized Litho3 parameters can then be input into the recipe set up to enable a real-time inspection, as such fab user can timely access the defect criticality information for subsequent defect disposition. In the interest of printability validation, such defect information and associated coordinates can be passed onto defect review via XLINK for further analysis. Corresponding MEEF values are also available for all identified critical defects. Through this automatic program the set up time for Litho3 can be reduced by up to 90%.For high capacity production fabs running a pre-inspection is deemed infeasible; this automatic optimization program can also serve as a direct interpretation of any regular reticle inspection even without invoking Litho3 set up, yet in the end provide output in the context of defect criticality. Results acquired from this program were found in good accordance with those from the real-time Litho3 inspection, for both critical and non-critical layers of 90 nm design node. Such capability allows detailed study of defect criticality in relation to its size, defect optical transmittance, residing surface, its proximity to a printing pattern as well as lithography parameters such as NA and sigma. Furthermore, coupling this automatic program with high resolution inspection also assists in determining lithography process window and an indepth comprehension of defect progression mechanism.

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Section: Contamination Overviewmentioning

confidence: 99%

Automatic optimization of MEEF-driven defect disposition for contamination inspection challenges

et al. 2007

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“…Over the last several years we have focused on understanding the relationships between lithographic exposure, cleaning and the chemical composition of the reticle surfaces, to include the quartz and metal surfaces and their effects on mask degradation and contamination, often referred to as haze [1][2][3] . The resulting work has allowed us to better understand the effects of micro-contamination on both the performance and life span of production reticles.…”

Section: Introductionmentioning

confidence: 99%

Challenges associated with advanced mask cleaning

Grenon¹

2011

SPIE Proceedings

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Historically, photomask and wafer cleaning have been considered trivial tasks. The primary challenge had been simply the removal of particles that may have an impact on final product yield. As wavelengths decrease and energy on the image plane of the reticle increase, the degree and complexity of surface contaminants on the reticle become a more complicated challenge. The result is that the mask fabricator is faced with two new challenges; reducing and identifying the types of contaminants on the reticle prior to exposure in the wafer fab and eliminating contaminants that have been deposited in the fab. These contaminants are often different in that 193nm exposure produces higher molecular weight contaminants that can be more difficult to remove. While the effects of these contaminants may not be serious for transmissive lithography, their effects can be catastrophic for reflective lithography, such as Extreme UltraViolet (EUV) lithography. We will provide results showing the types of contaminants commonly found on various types of reticles and the respective challenges associated with their removal. Additionally, prior to developing an effective cleaning protocol understanding the chemical composition of the substrate is extremely important. We will provide analytical data that will clearly describe the composition of the mask substrates.

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“…This gives rise to numerous challenges in the semiconductor wafer fabrication plants. Some of these challenges being: contamination (mainly haze [1,2] and particles), mask pattern degradation (MoSi oxidation [3], chrome migration [4], etc.) and pellicle degradation [5].…”

Section: Introductionmentioning

confidence: 99%

Mask degradation monitoring with aerial mask inspector

Tseng

et al. 2013

SPIE Proceedings

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As design rule continues to shrink, microlithography is becoming more challenging and the photomasks need to comply with high scanner laser energy, low CDU, and ever more aggressive RETs. This give rise to numerous challenges in the semiconductor wafer fabrication plants. Some of these challenges being contamination (mainly haze and particles), mask pattern degradation (MoSi oxidation, chrome migration, etc.) and pellicle degradation. Fabs are constantly working to establish an efficient methodology to manage these challenges mainly using mask inspection, wafer inspection, SEM review and CD SEMs. Aerial technology offers a unique opportunity to address the above mask related challenges using one tool. The Applied Materials Aera3™ system has the inherent ability to inspect for defects (haze, particles, etc.), and track mask degradation (e.g. CDU). This paper focuses on haze monitoring, which is still a significant challenge in semiconductor manufacturing, and mask degradation effects that are starting to emerge as the next challenge for high volume semiconductor manufacturers. The paper describes Aerial inspector (Aera3) early haze methodology and mask degradation tracking related to high volume manufacturing. These will be demonstrated on memory products.At the end of the paper we take a brief look on subsequent work currently conducted on the more general issue of photo mask degradation monitoring by means of an Aerial inspector.

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Formation and detection of subpellicle defects by exposure to DUV system illumination

Cited by 16 publications

References 0 publications

Automatic optimization of MEEF-driven defect disposition for contamination inspection challenges

Automatic optimization of MEEF-driven defect disposition for contamination inspection challenges

Challenges associated with advanced mask cleaning

Mask degradation monitoring with aerial mask inspector

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