7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Predictions for High Mobility Amorphous ITO(In2O3:Sn) Filmsvia Hybrid Machine Learning Model
Kunihiro Kamataki, Naho Itagaki, Daisuke Yamashita, Takamasa Okumura, Naoto Yamashita,
Kazunori Koga, Masaharu Shiratani
1Graduate School and Faculty of Information Science and Electrical Engineering,
Kyushu University
e-mail (speaker):kamataki@plasma.ed.kyushu-u.ac.jp
1. Introduction
In semiconductor manufacturing, plasma processes ofCVD, sputtering, and etching, play central roles. Theseprocesses have dozens of tuning parameters and multiple
objective variables for product evaluation. Relations
between the tuning parameters and objective variables arehighly complicated and thus are hard to be interpreted.
Since plasma process has a non-linear relationshipbetween these tuning parameters and plasma parameters
and a complex relationship between the plasma andmaterial interface/property of material, to identify
important experimental tuning parameter for the quick
production of the desired material requires considerable
efforts by well-established researchers and can be abottleneck of the research and development.
In general, plasma processes demand many externalparameters to be tuned via trial-and-error, and the numberof tuning parameters can be enormous in some practicalcases. Physical and chemical parameters of reactive
plasma in a reactor and products also have many
characteristics. In such cases, data-based statistical ormachine learning approach offers a novel way for tuning
plasma processes in a short period.
Recently, there has been growing interest in amorphous
In2O3:Sn (a-ITO) due to its unique advantages, including
surface smoothness, good short-range uniformity, lowinternal stress, and high etching rate, which all stem fromits amorphous structures [1]. However, conventional aITO films have much lower mobility than polycrystalline
ITO (pc-ITO) films, limiting their practical use. Moreover,the instability of amorphous phases of ITO thin films andtheir tendency to crystallize at substrate temperatures
around 150-200 °C pose additional limitations on thepractical utilization of a-ITO films in various devices. To
address these challenges, we have recently developed anovel fabrication method called nitrogen-mediatedamorphization (NMA) method that can produce a-ITO
films with higher mobility and improved thermal stability
than conventional methods [2,3]. However, since theNMA method for a-ITO thin film fabrication is a newcontrol one of crystal growth that is not restricted by thecrystallization temperature. There is not muchinformation available on the experimental conditions
required for amorphization and boundary conditions
between amorphous and crystalline materials. Thus, inthis study, we propose the hybrid model incorporatingmachine learning techniques, such as classification andregression models, as an efficient approach for
determining theboundary conditions and optimal
sputtering conditions to achieve high mobility a-ITO thinfilms.

2. Method
This hybrid model that was consisted of support vector
machine (SVM)[4] as a classification model and gradientboosting regression tree (GBRT)[5] as a regression model.
SVM is a powerful classification model that is particularly
useful when working with high-dimensional data. It works
by finding the hyperplane that maximally separates thetwo classes in the feature space. GBRT is an ensemble
learning technique that leverages multiple decision trees
to create a more accurate predictive model. In GBDT,
decision trees are trained in a sequential manner, with eachsubsequent tree aiming to correct the errors made by theprevious tree. ITO films were fabricated on quartz glass
substrates by radio-frequency (RF) magnetron sputteringwith nitrogen-mediated amorphization (NMA) method.810
N2/Ar flow ratio, which is the feature of nitrogen
mediated amorphization method, ranges from 0 % to12.5 %. The vertical position of the substrate iscontrollable in 55-105 mm of target-substrate distance(gap). The substrate temperature varies from room
temperature (~30 ℃) to 300 ℃. The sputtering target was
ITO (In2O3:Sn) or In2O3. These four parameters (N2/Arflow ratio, gap, substrate temperature, type of target) areused as the features which means the variables for training
predictive models and performing predictions. Among the203 thin film datasets examined in this study, 26%
correspond to crystalline thin films, while 74% represent
amorphous ones.
3. Results and Discussion
The hybrid results between SVM and GBRT models forthe fabrication of ITO thin films show the boundary
conditions between amorphous and crystalline
crystallinity and optimal thin film deposition conditions
that resulted in a-ITO films with 27% higher mobility near
the boundary than previous research results. Thus, thisprediction model identified key parameters and optimal
sputtering conditions necessary for producing highmobility a-ITO films. The identification of such boundary
conditions through machine learning is crucial in theexploration of thin film properties and enables thedevelopment of high-throughput experimental designs.
References[1] K. Imoto, et. al., in Proceedings of 40th International
Symposium on Dry Process (DPS) P62 (2018). [2] N.
Itagaki, et. al., in Proceeding of 9th International
Symposium
onControlofSemiconductor
Interfaces(ISCI-IX), TuP2-4 (2022). [3] N. Itagaki, et. al.,Scientific reports, 10, 4669 (2020). [4] V.
Sundararaghavan, N. Zabaras, Computational Materials
Science, 32, 2, 223 (2005). [5] J. H. Friedman, The Annalsof Statistics, 29, 5, 1189 (2001).