A  model-based, Bayesian approach to the CF4/Ar etch of SiO2

Chopra, Meghali; Helpert, Sofia; Verma, Rahul; Zhang, Zizhuo; Zhu, Xilan; Bonnecaze, Roger T.

doi:10.1117/12.2297482

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…In general, the coefficient of determination is considered as high according to the interpretation of the R value, denoting that R ≥ 0.7 (i.e., R 2 ≥ 0.49) can be regarded as "a strong linear relationship" [17]. In addition, the value was equivalent to that of the prediction by Chopra et al [7]. They achieved a similar coefficient of determination (R 2 = 0.63) to predict the etching rate of SiO 2 with CF 4 /Ar gasses.…”

Section: B Validation Of Our Methodologymentioning

confidence: 99%

“…Chopra et al developed a software tool This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ for creating plasma etch recipes based on physical models and Bayesian inference [6] and applied it to prediction of the etching results [7]. They showed that the etching rate of SiO 2 with CF 4 /Ar gasses could be predicted (R 2 = 0.63).…”

Section: Introductionmentioning

confidence: 99%

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Materials Informatics for Process and Material Co-Optimization

Tomita

Sato

Yoshii

et al. 2019

IEEE Trans. Semicond. Manufact.

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In semiconductor manufacturing, fabrication processes and their materials should be properly co-optimized to achieve required processing results within reasonable development duration and acceptable cost. Unfortunately, it is a very time-consuming procedure, because the number of possible combinations of process/material candidates is very large. Here, we develop a methodology for co-optimization of processes and their materials. We successfully constructed a prediction model for dry-etching of high-k materials (R 2 = 0.65). Also, it was proven that considering both the materials and processes is needed for accurate prediction of etching rates. By trying only <0.00001% of all possible process/material candidates with this model and Bayesian optimization, we can find new combinations of gasses and their processes for more than 100 times higher etching rates than that with a traditional gas/process condition. Furthermore, we discussed that accurate prediction can be made by using a combination of the Bayesian optimization with LASSO and materials knowledge from related scientific papers. Future work will focus on validating the versatility of our methodology by applying it to other development items.

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Section: B Validation Of Our Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Materials Informatics for Process and Material Co-Optimization

Tomita

Sato

Yoshii

et al. 2019

IEEE Trans. Semicond. Manufact.

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show abstract

“…Collectively, atoms, ions and molecules are hereafter referred to as molecular entities. These rates can depend strongly on different geometrical effects and transport phenomena inside the reactor [52]. The way in which molecular entities traverse the reactor depends on their specific properties, as well as on the thermodynamics of the chosen process.…”

Section: Methodsmentioning

confidence: 99%

Modeling of Gate Stack Patterning for Advanced Technology Nodes: A Review

2018

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Semiconductor device dimensions have been decreasing steadily over the past several decades, generating the need to overcome fundamental limitations of both the materials they are made of and the fabrication techniques used to build them. Modern metal gates are no longer a simple polysilicon layer, but rather consist of a stack of several different materials, often requiring multiple processing steps each, to obtain the characteristics needed for stable operation. In order to better understand the underlying mechanics and predict the potential of new methods and materials, technology computer aided design has become increasingly important. This review will discuss the fundamental methods, used to describe expected topology changes, and their respective benefits and limitations. In particular, common techniques used for effective modeling of the transport of molecular entities using numerical particle ray tracing in the feature scale region will be reviewed, taking into account the limitations they impose on chemical modeling. The modeling of surface chemistries and recent advances therein, which have enabled the identification of dominant etch mechanisms and the development of sophisticated chemical models, is further presented. Finally, recent advances in the modeling of gate stack pattering using advanced geometries in the feature scale are discussed, taking note of the underlying methods and their limitations, which still need to be overcome and are actively investigated.

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“…Materials integration has also shown promising results in semiconductor manufacturing. Chopra et al developed a software tool for creating plasma etch recipes based on physical models and Bayesian inference (8) and applied it to prediction of experimental results (9). They showed that the etching rate of SiO2 with CF4/Ar gasses could be predicted.…”

Section: Introductionmentioning

confidence: 99%

Reactive Force-Field Molecular Dynamics Study of SiGe Thin Film Growth in Plasma Enhanced Chemical Vapor Deposition Processes

Uene

Mabuchi²,

Zaitsu

et al. 2020

ECS Trans.

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The SiGe thin films, such as hydrogenated amorphous SiGe (a-SiGe:H) and hydrogenated microcrystalline SiGe (μc-SiGe:H), are known as a potential material. In order to form promising SiGe thin films using chemical vapor deposition, relationship between materials/process and structure/composition should be clarified at the atomic level. We analyzed the influence of the substrate temperature and the ratio of SiH3 and GeH3, which are considered to be the dominant gaseous species on the deposition, on the crystallinity and atomic content in SiGe thin films by reactive force-field molecular dynamics simulations. The crystallinity increases as the substrate temperature increases and reaches its minimum value when the ratio of SiH3 and GeH3 is approximately 0.5. The hydrogen content decreased as the substrate temperature increases, while silicon and germanium content tended to increase as substrate temperature increase. These results were in good agreement with relevant studies.

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A model-based, Bayesian approach to the CF4/Ar etch of SiO2

Cited by 6 publications

References 17 publications

Materials Informatics for Process and Material Co-Optimization

Materials Informatics for Process and Material Co-Optimization

Modeling of Gate Stack Patterning for Advanced Technology Nodes: A Review

Reactive Force-Field Molecular Dynamics Study of SiGe Thin Film Growth in Plasma Enhanced Chemical Vapor Deposition Processes

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