In-Situ Monitoring of Multiple Oxide/Nitride Dielectric Stack PECVD Deposition Process

Jang, Dong Beom; Hong, Sang Jeen

doi:10.1007/s42341-018-0005-0

Cited by 13 publications

(9 citation statements)

References 6 publications

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“…7, layers were deposited in the same chamber using the same method. However, the thickness of the layers gradually increased owing to the aforementioned reasons [19].…”

Section: Resultsmentioning

confidence: 99%

“…The main challenge in 3D-NAND manufacturing is to form the required MOLD dielectric films exactly over the substrate. It has been reported that the thickness of actual deposited films tends to gradually increase in plasma enhanced chemical vapor deposition (PECVD) even though the films are deposited using the same process [2]. Hence, the fast and accurate measurement of the thickness of MOLD films has become increasingly important to improve the quality of the plasma deposition process.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network Modeling of Multiple Oxide/Nitride Deposited Dielectric Films for 3D-NAND Flash

Choi

Ho²,

Lee

et al. 2020

Appl. Sci. Converg. Technol.

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The fast and accurate measurement of the thickness of multiple oxide/nitride layer deposition (MOLD) films is desirable to improve the quality of the plasma deposition process and potentially simplify the metrology in 3D NAND flash memory devices. In this study, we performed deep neural network modeling of the reflectance spectrum data of two pairs of oxide/nitride films on a silicon substrate. We designed a deep neural network model to estimate the thickness of four stacked thin-film layers and the MOLD film. Principle component analysis of this model was performed to develop another model with 27 features. Finally, a combined model was designed by fine tuning both the models and applying an ensemble algorithm to both. The mean absolute error of the combined predictive model was lower than that of the individual models. We verified the performance of the proposed model by considering the thin-film deposition mechanism with respect to the infrared reflectance metrology of MOLD thin films. This study demonstrates the potential of machine learning for predicting the thickness of multiple layered films, addressing the limitations of optical metrology.

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“…7, layers were deposited in the same chamber using the same method. However, the thickness of the layers gradually increased owing to the aforementioned reasons [19].…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Modeling of Multiple Oxide/Nitride Deposited Dielectric Films for 3D-NAND Flash

Choi

Ho²,

Lee

et al. 2020

Appl. Sci. Converg. Technol.

Self Cite

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“…They can be a solution of the finer levels of control and diagnostics of complex processes [11,12]. The use of sensors that enable the monitoring of plasma, which directly affects the actual process, has made an excellent contribution to the APC system, and studies using sensors such as RF sensors, optical sensors, and various types of probes have been conducted [13,14]. Numerous measurement processes in complex processes are essential, but have a devastating effect on time and cost, and research on virtual metrology has been actively conducted to solve this problem [15,16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

et al. 2021

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Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

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“…Plasma-enhanced chemical vapor deposition (PECVD) is one of the major deposition processes for fabricating various thin films such as oxide-nitride-oxide-nitride films, amorphous carbon layers, and antireflective coatings used in three-dimensional vertical NAND flash memory and other semiconductor devices [1][2][3]. The increasing demand for memory devices with expanded storage capacity [4] has driven the need for highly stacked uniform thin films [5], which require the precise control of the temperature of wafers during the PECVD process. Because temperature uniformity and the ramping-up/-down rata of the PECVD equipment critically determine yield and fabrication throughput [6][7][8][9][10][11], they are the main evaluation criteria of the capability of PECVD equipment and wafer chucks (or heaters).…”

Section: Introductionmentioning

confidence: 99%

Fabrication of Planar Heating Chuck Using Nichrome Thin Film as Heating Element for PECVD Equipment

Yoon

Min

et al. 2021

Electronics

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Improving semiconductor equipment and components is an important goal of semiconductor manufacture. Especially during the deposition process, the temperature of the wafer must be precisely controlled to form a uniform thin film. In the conventional plasma-enhanced chemical vapor deposition (PECVD) chuck, heating rate, and temperature uniformity are limited by the spiral pattern and volume of the heating element. To overcome the structural limitation of the heating element of conventional chuck, we tried to develop the planar heating chuck (PHC), a 6-inch PECVD chuck with a planar heating element based on NiCr thin film that would be a good candidate for rapidly and uniformly heating. The time for the temperature elevation from room temperature to 330 °C was 398 s. In a performance evaluation, the fabricated PHC successfully completed a SiO2 PECVD process.

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In-Situ Monitoring of Multiple Oxide/Nitride Dielectric Stack PECVD Deposition Process

Cited by 13 publications

References 6 publications

Deep Neural Network Modeling of Multiple Oxide/Nitride Deposited Dielectric Films for 3D-NAND Flash

Deep Neural Network Modeling of Multiple Oxide/Nitride Deposited Dielectric Films for 3D-NAND Flash

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

Fabrication of Planar Heating Chuck Using Nichrome Thin Film as Heating Element for PECVD Equipment

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