Accelerating on-device overlay metrology accuracy verification

Abramovitz, Yaniv; Sarig, Lior; Levin, G. Ia.; Levi, Shimon; Davidescu, Ron; Harel, Daniel; Adan, Ofer; Leray, Philippe; Halder, Sandip

doi:10.1117/12.2568682

Cited by 2 publications

(3 citation statements)

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“…Recent e-Beam technologies, enable better performance control by allowing massive sampling in high throughput & resolution [2] . In addition to those measurement, our research exploring a parallel path to utilize data existing in those same images, by using full image deep learning technics.…”

Section: Methodsmentioning

confidence: 99%

“…One of the main metrics to perform metrology to e-test correlation is the Edge Placement Error (EPE). The EPE is a well-accepted metric which can be derived from CD, Overlay and LER measurements from more patterning layers that concerned [2] . Therefore, real time EPE measurement becomes essential for both monitoring and controlling pattern fidelity.…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

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Direct yield prediction from SEM images

Choona

Linshiz

Pres

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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In line Electrical measurement (E-Test) are the most effective predictors for EOL yield control. As technology progress with scaling, the number. of process layers increases, allowing in-line electrical measurements only after several months since lot started process in-line. As a result, each E-Test monitor controls longer and more challenging process loop. Most of the in-line pattern control that impact electrical performance measured separately for each pattern polygon and material properties. In addition, Edge Placement Error (EPE) methodology, allows combination of multiple dimensions like CD, Overlay and LER measurements to better predict yield impact. Technology shrinkage, resulting that transistor electrical performance, defined by more geomaterial parameters as well as material compositions and defectivity. In this paper we demonstrate a direct prediction from high resolution Scanning Electron Microscope (SEM) images to the first inline electrical measurement (M1) using Deep Learning (DL) techniques. The DL model provide early prediction of electrical performance, describing accurately Within Wafer (WIW) variation weeks earlier than the actual electrical measurements. Multiple layers prediction may indicate suspected process loop that modulate majority of variation and save time to solution. It can be achieved since the DL model utilizes complementary information exist on the full e-Beam image like materials and defectivity. The following results will indicate that accumulating information collected from several layers will improve prediction sensitivity and lead to even more accurate prediction capabilities. We assume that the effectiveness of the proposed prediction method will increase with process complexity, since the modulation of the existing yield predictors is losing sensitivity as design rule shrinks. In addition, since fabrication phase gets longer, the time to actual electrical measurements increase, making an early, nondestructive, and accurate prediction for electrical performance more and more valuable.

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Section: Methodsmentioning

confidence: 99%

Section: Introduction 11 Backgroundmentioning

confidence: 99%

Direct yield prediction from SEM images

Choona

Linshiz

Pres

et al. 2023

Metrology, Inspection, and Process Control XXXVII

View full text Add to dashboard Cite

show abstract

“…Abramovitz et al [76] have proposed a flow of SEM overlay verification using inline FIB that was performed in a fullscale wafer and a short time to results. In the flow, first the wafer overlay was measured using the EB instrument with high energy PE.…”

Section: Overlaymentioning

confidence: 99%

Electron microscopy in semiconductor inspection

Nakamae

2021

Meas. Sci. Technol.

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Currently, semiconductor devices are manufactured in a technology node of several nanometers. Electron microscopy is mainly used in semiconductor inspection in manufacturing stages since accelerated electrons have wavelengths of nanometers or less, and a high spatial resolution can be expected. Among various electron microscopes since the scanning electron microscope (SEM) can observe the sample as it is without processing the sample, the SEM-based inspection instrument is mainly used at each stage of manufacturing the semiconductor device. The paper presents a review of SEM-based electron microscopy in semiconductor inspection. First, an overview of electron microscopy is described to understand the electron-sample interaction, the characteristics of electrons emitted from an irradiated specimen, charging, noise, and so on. Next, application areas such as mask inspection are introduced. Finally, future challenges are discussed.

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Accelerating on-device overlay metrology accuracy verification

Cited by 2 publications

References 0 publications

Direct yield prediction from SEM images

Direct yield prediction from SEM images

Electron microscopy in semiconductor inspection

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