Paper
1 November 2007 To improve reticle re-qualification process and reduce reticle re-cleaning frequency using efficient defect classification and defect tracking
Eric Haodong Lu, Jim Wang, Raj Badoni, Ellison Chen, Weimin Ma
Author Affiliations +
Abstract
For improving productivity and reducing manufacturing cost, it is critical for wafer fabs to reduce the frequency of reticle re-clean and control the risk of missing defects of lithographic significance from overall haze defected. Haze classification and haze behavior monitoring are highly time consuming processes. Many wafer fabs skip such operations and instead re-clean reticles frequently in order to reduce the risk of missing killer haze defects. Such Reticle Re-Qual rule leads to more than necessary reticle re-cleaning, shortening the life cycle of reticles and increasing the manufacturing cost. In this paper, we investigate an efficient defect classification method - ReviewSmart, and defect auto tracking method to classify defects and efficient tracking haze growth. A solution is discussed for wafer fabs to monitor haze behaviors and improve Reticle Re-Qual rules for controlling and reducing manufacturing cost at lower risk. A total of more then 30 production reticles of critical layers of OD, Poly, Contact and Metal 1 were inspected by STARLight2TM on KLA-Tencor TeraScan SL516 system. ReviewSmart processed all the defects detected during Reticle Re-Qual inspection. The results showed significant reduction in defect review times, with 100% fidelity rate.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eric Haodong Lu, Jim Wang, Raj Badoni, Ellison Chen, and Weimin Ma "To improve reticle re-qualification process and reduce reticle re-cleaning frequency using efficient defect classification and defect tracking", Proc. SPIE 6730, Photomask Technology 2007, 67303Q (1 November 2007); https://doi.org/10.1117/12.747195
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KEYWORDS
Air contamination

Reticles

Semiconducting wafers

Inspection

Remote sensing

Defect detection

Manufacturing

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