Wafer Redeposition Impact on Etch Rate Uniformity in IPVD System

Brčka, Jozef; Robison, R.L.

doi:10.1109/tps.2006.889294

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…2 During chemical vapor deposition step, species depletion and temperature non-uniformity on the wafer at lower temperatures may cause thickness non-uniformity [33], [34]. Redeposition effect in physical vapor deposition [35] may cause non-uniformity of etch rate. Moreover, center peak shape of the RF electric field distribution [36] also leads to a center peak shape of etch rate, and chamber wall conditions [37] also cause etch rate non-uniformity.…”

Section: Physical Origins Of Spatial Variationmentioning

confidence: 99%

Physically justifiable die-level modeling of spatial variation in view of systematic across wafer variability

Cheng

Gupta

Spanos

et al. 2009

Proceedings of the 46th Annual Design Automation Conference

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Abstract-Modeling spatial variation is important for statistical analysis. Most existing works model spatial variation as spatially correlated random variables. We discuss process origins of spatial variability, all of which indicate that spatial variation comes from deterministic across-wafer variation, and purely random spatial variation is not significant. We analytically study the impact of across-wafer variation and show how it gives an appearance of correlation. We have developed a new die-level variation model considering deterministic across-wafer variation and derived the range of conditions under which ignoring spatial variation altogether may be acceptable. Experimental results show that for statistical timing and leakage analysis, our model is within 2% and 5% error from exact simulation result, respectively, while the error of the existing distance-based spatial variation model is up to 6.5% and 17%, respectively. Moreover, our new model is also 6× faster than the spatial variation model for statistical timing analysis and 7× faster for statistical leakage analysis.

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Section: Physical Origins Of Spatial Variationmentioning

confidence: 99%

Physically justifiable die-level modeling of spatial variation in view of systematic across wafer variability

Cheng

Gupta

Spanos

et al. 2009

Proceedings of the 46th Annual Design Automation Conference

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“…For wafer-level variation associated with several process steps such as etching [13], [16] and rapid thermal annealing [17], [18], it is observed that edge dies are often substantially different from other parts of the wafer [13], in addition to the effects that can be modeled using a quadratic function. We capture the edge effect by supplementing quadratic functions with the following indicator functions:…”

Section: A Physical Dictionarymentioning

confidence: 99%

Efficient Spatial Pattern Analysis for Variation Decomposition Via Robust Sparse Regression

Zhang

Balakrishnan

et al. 2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-In this paper, we propose a new technique to achieve accurate decomposition of process variation by efficiently performing spatial pattern analysis. We demonstrate that the spatially correlated systematic variation can be accurately represented by the linear combination of a small number of templates. Based on this observation, an efficient sparse regression algorithm is developed to accurately extract the most adequate templates to represent spatially correlated variation. In addition, a robust sparse regression algorithm is proposed to automatically remove measurement outliers. We further develop a fast numerical algorithm that may reduce the computational time by several orders of magnitude over the traditional direct implementation. Our experimental results based on both synthetic and silicon data demonstrate that the proposed sparse regression technique can capture spatially correlated variation patterns with high accuracy and efficiency.

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Section: Physical Origins Of Spatial Variationmentioning

confidence: 99%

“…In real processes, the wafers are rotated to improve uniformity. [35] and [37] showed that the etch rate varies radially across the wafer: the etch rate is high at the center of the wafer and decreases toward the edges. Post-exposure bake (PEB) temperatures are higher at the center of the wafer and decreases outwards [38].…”

Section: Physical Origins Of Spatial Variationmentioning

confidence: 99%

Physically Justifiable Die-Level Modeling of Spatial Variation in View of Systematic Across Wafer Variability

Cheng¹,

Gupta

Spanos

et al. 2011

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

Modeling spatial variation is important for statistical analysis. Most existing works model spatial variation as spatially correlated random variables. We discuss process origins of spatial variability, all of which indicate that spatial variation comes from deterministic across-wafer variation, and purely random spatial variation is not significant. We analytically study the impact of across-wafer variation and show how it gives an appearance of correlation. We have developed a new die-level variation model considering deterministic across-wafer variation and derived the range of conditions under which ignoring spatial variation altogether may be acceptable. Experimental results show that for statistical timing and leakage analysis, our model is within 2% and 5% error from exact simulation result, respectively, while the error of the existing distance-based spatial variation model is up to 6.5% and 17%, respectively. Moreover, our new model is also 6× faster than the spatial variation model for statistical timing analysis and 7× faster for statistical leakage analysis.

show abstract

Wafer Redeposition Impact on Etch Rate Uniformity in IPVD System

Cited by 7 publications

References 9 publications

Physically justifiable die-level modeling of spatial variation in view of systematic across wafer variability

Physically justifiable die-level modeling of spatial variation in view of systematic across wafer variability

Efficient Spatial Pattern Analysis for Variation Decomposition Via Robust Sparse Regression

Physically Justifiable Die-Level Modeling of Spatial Variation in View of Systematic Across Wafer Variability

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