Electronic Convergence Materials and Device Research Center, Korea Electronics Technology Institute, 25, Saenari-ro, Bundang-gu, Seongnam 13509, Republic of Korea.
Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Republic of Korea.
Langmuir. 2023 Apr 11;39(14):4984-4992. doi: 10.1021/acs.langmuir.2c03465. Epub 2023 Mar 22.
Extreme gradient boosting (XGBoost) is an artificial intelligence algorithm capable of high accuracy and low inference time. The current study applies this XGBoost to the production of platinum nano-film coating through atomic layer deposition (ALD). In order to generate a database for model development, platinum is coated on α-Al2O3 using a rotary-type ALD equipment. The process is controlled by four parameters: process temperature, stop valve time, precursor pulse time, and reactant pulse time. A total of 625 samples according to different process conditions are obtained. The ALD coating index is used as the Al/Pt component ratio through ICP-AES analysis during postprocessing. The four process parameters serve as the input data and produces the Al/Pt component ratio as the output data. The postprocessed data set is randomly divided into 500 training samples and 125 test samples. XGBoost demonstrates 99.9% accuracy and a coefficient of determination of 0.99. The inference time is lower than that of random forest regression, in addition to a higher prediction safety than that of the light gradient boosting machine.
极端梯度提升（XGBoost）是一种能够实现高精度和低推理时间的人工智能算法。本研究将 XGBoost 应用于通过原子层沉积（ALD）生产铂纳米薄膜涂层。为了生成模型开发的数据库，通过旋转式 ALD 设备在 α-Al2O3 上镀铂。该过程由四个参数控制：工艺温度、截止阀时间、前体脉冲时间和反应物脉冲时间。根据不同的工艺条件，共获得了 625 个样本。ALD 涂层指数通过后处理中的 ICP-AES 分析用作 Al/Pt 成分比。将四个工艺参数作为输入数据，并将 Al/Pt 成分比作为输出数据。经过后处理的数据集随机分为 500 个训练样本和 125 个测试样本。XGBoost 的准确率为 99.9%，决定系数为 0.99。推理时间低于随机森林回归，此外，预测安全性高于轻梯度提升机。
