Full Wafer Stress Metrology for Dielectric Film Characterization: Use Case

Brouzet, V.; Gredy, Victor; Chenevas-Paule, F.; Le-Chao, K.; Guiheux, Denis; Laurent, Antoine; Coutellier, Vincent; Le-Cunff, Delphine

doi:10.1109/asmc.2019.8791784

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Blanket nitride layers on silicon can easily be characterized by industrial in-line ellipsometry as already demonstrated in [5]. For this specific use case, a strong inhomogeneity was observed linked to changes in the refractive index of the layer (Fig.…”

Section: ) Blanket Wafer Analysismentioning

confidence: 61%

Spectroscopic Ellipsometry Imaging for Process Deviation Detection via Machine Learning Approach

Alcaire¹,

Cunff²,

Gredy³

et al. 2020

2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

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Spectroscopic ellipsometry is a very sensitive metrology technique to accurately measure the thickness and the refractive index of the different layers present on specific dedicated metrology targets. In parallel, optical defectivity techniques are widely implemented in production lines to inspect a large number of dies and catch physical and patterning defects during the process flow. It becomes then of interest to explore a new approach overlapping metrology and defectivity by using the sensitivity of metrology tools on a full wafer scale. In our case, spectroscopic ellipsometry's optical response was collected directly on the dies to capture specific deviations such as film properties and thickness variation. This is an innovative strategy that requires a model-less approach, combining an automatic ellipsometry mapping generation and a smart classification via a machine learning algorithm. In this paper, we will present such approach on two industrial use cases and explain how an image classification algorithm can be implemented to automatically detect the process drift on the latter.

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Section: ) Blanket Wafer Analysismentioning

confidence: 61%

Spectroscopic Ellipsometry Imaging for Process Deviation Detection via Machine Learning Approach

Alcaire¹,

Cunff²,

Gredy³

et al. 2020

2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

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“…SOI Trap Rich wafers were processed using the entire 65nm technological process flow and successively monitored after anneals and lithography steps. Slip lines were measured using a patterned wafer geometry tool recently developed to inline measure patterned wafers during their processing (13)(14). The threshold for slip line detection has been empirically set to 1nm to eliminate noise and false detection.…”

Section: Slip Line Results and Deformationmentioning

confidence: 99%

Impact of Oxygen on the Generation of Slip Lines and the Electronic Properties of Si-based Substrates

Abbadie,

Pribat,

Gredy

et al. 2023

ECS Trans.

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We focus on slip line formation and propagation on different types of Si and SOI substrates that underwent high temperature anneals during 65nm RFSOI device fabrication. Different parameters are found to contribute to slip line formation and dislocations propagation, mainly the substrate properties (thickness, layer and oxygen in silicon handle), the temperature gradients and the support between furnace carriers and backsides of substrates. Several parameters contribute to substrate deformation, e.g. the warpage increase. The warp degradation is found to be directly linked to low oxygen concentrations in silicon handle. This understanding is very helpful to introduce and process new substrates in advanced front-end process flows for MOS device fabrication.

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“…For the first use case, we tested the sensitivity of our modelless ellipsometry approach to detect intra wafer signatures resulting from stress of nitride layer [5] (both on single layer of nitride on silicon and on the same nitride layer deposited on a product wafer). The nitride layers used in this study are dielectric films acting as isolation between metallic aluminum contact pads of patterned wafer.…”

Section: A Intra-wafer Nitride Passivation Layers Properties Variationmentioning

confidence: 99%

“…It is to be acknowledged here that the orientation of the signatures can differ between blanket and product wafer due to the absence of notch alignment in the nitride deposition chamber. The results were compared to the characterization results obtained with a full wafer interferometry-based metrology technique which notably measures local stress and nano-topography [5]. The stress cartography is shown in Fig.…”

Section: ) Product Wafer Analysismentioning

confidence: 99%

On the Fly Ellipsometry Imaging for Process Deviation Detection

Alcaire

Cunff

Soulan

et al. 2022

IEEE Trans. Semicond. Manufact.

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Spectroscopic ellipsometry is a very sensitive optical metrology technique commonly used in semiconductor manufacturing lines to accurately measure the thickness and refractive index of different layers present on specific dedicated metrology targets on the wafers. In parallel, optical defectivity techniques are widely implemented in production lines to inspect a significant amount of dies representative of the full wafer and detect physical and patterning defects. A new approach can then simply emerge which is to apply ellipsometry metrology techniques at a full or die wafer scale. This strategy, at the frontier between metrology and defectivity field is expected to bring solutions for certain types of process deviation. In our case, ellipsometry's optical response was collected on large areas of product wafers to capture specific deviations such as film properties, thickness, and patterning variation. This is an innovative strategy that relies on a model-less approach to detect process drifts, using ellipsometry's sensitivity to material properties and design architecture variations. In this paper, we will present this approach on three industrial cases.

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Full Wafer Stress Metrology for Dielectric Film Characterization: Use Case

Cited by 8 publications

References 7 publications

Spectroscopic Ellipsometry Imaging for Process Deviation Detection via Machine Learning Approach

Spectroscopic Ellipsometry Imaging for Process Deviation Detection via Machine Learning Approach

Impact of Oxygen on the Generation of Slip Lines and the Electronic Properties of Si-based Substrates

On the Fly Ellipsometry Imaging for Process Deviation Detection

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