Abstract
In the modern semiconductor industry, defective products occur with unexpected small variables due to process miniaturization. Managing the condition of each part is an effective way of preventing unexpected errors. The industrial internet of things (IIoT) environment, which can monitor and analyze the performance degradation of parts that affect process results, enables advanced process yield management. This paper introduces the IIoT concept-based data monitoring and diagnostic system construction results. The process of pump vibration data acquisition is explained to evaluate the effectiveness of this system. The target process is deposition. The purpose of the system is to detect degradation of pumps due to by-products of the atomic layer deposition (ALD) process. The system consists of three areas: a data acquisition unit using six vibration sensors, a Web access-based monitoring unit that can monitor vibration data, and an Azure platform that searches for outliers in vibration data.
Highlights
Layer Deposition Equipment.Internet of things (IoT) is a technological innovation that creates an environment of convergence around nature
We present a realistic practice of the in situ monitoring and diagnosis of the dry vacuum pump installed at the semiconductor fabrication equipment within the scope of the practical application of industrial internet of things (IIoT)
This paper introduced asystem system monitor and analyze pump failure as aby-product by-product
Summary
The role of a dry vacuum pump is to maintain a constant pressure level as an auxiliary facility; when it goes wrong, one should pay for the expensive production loss in semiconductor chip manufacturing. The monitoring and diagnosis of dry vacuum pumps in semiconductor manufacturing with the scope of IIoT give many benefits to avoid misprocessing from unexpected component drift or failure. This research employed foreline pressure measurement to acquire pump status data in the perception layer of IIoT and data analysis for decision making using an artificial neural network as a middleware layer. We present a realistic practice of the in situ monitoring and diagnosis of the dry vacuum pump installed at the semiconductor fabrication equipment within the scope of the practical application of IIoT.
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