Failure prediction across process window for robust OPC

Shang, Shumay; Granik, Yuri; Cobb, Nicolas B.; Maurer, Wilhelm; Cui, Yuping; Liebmann, Lars W.; Oberschmidt, James; Singh, Rama N.; Vampatella, Ben R.

doi:10.1117/12.485478

Cited by 13 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[4][5][6] Resist collapse is also difficult to simulate properly. 1 Nevertheless, design rule check (DRC) is not immune to inaccuracies: design rules and their parameters are also defined with uncertainty and incertitude.…”

Section: Lithography Process Check and Design Rule Checkmentioning

confidence: 99%

The nebulous hotspot and algorithm variability

Wong

Lam

2009

SPIE Proceedings

View full text Add to dashboard Cite

Computation lithography relies on algorithms. These algorithms exhibit variability that can be as much as 5% (1 σ) of the critical dimension for the 65-nm technology. Using hotspot analysis and fixing as an example, such variability can be addressed on the algorithm level via controlling and eliminating its root causes, and on the application level by setting specifications that are commensurate with both the limitations of the algorithms and the goals of the application.

show abstract

Section: Lithography Process Check and Design Rule Checkmentioning

confidence: 99%

The nebulous hotspot and algorithm variability

Wong

Lam

2009

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…In a failure model [1] expressing the pattern variation dependence of the pattern failure, the model input is mainly Imax (the maximum of aerial image intensity) and Imin (the minimum of aerial image intensity). Therefore, we first tried to make a failure model using the Imax and Imin as the input.…”

Section: Imax-imin Modelmentioning

confidence: 99%

“…When the process window is restricted by the pattern failure as shown in the figure, it is very attractive if we can make a failure model capable of expressing the process condition dependence of experimental pass/fail data precisely and able to predict the process window. Although several authors have already made failure models [1]- [4] , the models are mainly for explaining not the process condition dependence but pattern variation dependence. It means that a model intended to explain the process condition dependence of the pattern failure has not yet been constructed.…”

Section: Introductionmentioning

confidence: 99%

A novel lithography design and verification methodology with patterning failure

et al. 2008

View full text Add to dashboard Cite

We made a new model for the pattern failure, which was the pattern collapse and the pattern bridging, of the resist patterns of 43nm 1:1 lines and spaces (L/S) exposed as a focus-exposure matrix, in order to explain and predict the process window of the pattern failure. It was found that the conventional "Imax-Imin" model was unable to be fitted to the experimental pass/fail data. Instead of the Imax and Imin, we selected the critical dimension (CD) and the normalized image log slope (NILS) as the model input. The new "CD-NILS" model corresponded well to the experimental pass/fail data. The good correspondence was assumed to be due to the properly selected model input. The pattern collapse, which occurs during the drying of the water at the rinse of the resist patterns, is expected to be accelerated by the smaller line CD and the larger line width roughness (LWR) due to smaller NILS. The pattern bridging, which occurs at the resist development, is expected to be accelerated by the larger line CD and the larger LWR. The CD-NILS model predicted the process window precisely when a new process condition (= a new illumination in this case) was adopted. It suggests that the CD-NILS model is a powerful methodology for predicting the process window in order to optimize the process condition and to optimize the lithography design.

show abstract

“…OPC verification is an effective solution to verify the correctness of full-chip OPC design [1][2], and to ensure that optical and process errors have not violated the design rules [3] and optical rules [4]. In Fab's early development stage, it is used for OPC recipe tuning and optimization experiments.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted effective OPC verification hotspot capture

Yin,

Chew,

Shang

et al. 2023

Photomask Technology 2023

View full text Add to dashboard Cite

Failure prediction across process window for robust OPC

Cited by 13 publications

References 0 publications

The nebulous hotspot and algorithm variability

The nebulous hotspot and algorithm variability

A novel lithography design and verification methodology with patterning failure

Machine learning assisted effective OPC verification hotspot capture

Contact Info

Product

Resources

About