Economic Control of Quality of Manufactured Product.

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.

Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wafer map data (i.e., without performing feature extraction). However, because increasingly more sensor data are analyzed during semiconductor fabrication, currently used approaches can be inadequate in processing large-scale data sets. Therefore, a set of novel rotation- and scale-invariant features is proposed for obtaining a reduced representation of wafer maps. Such features are crucial when employing WMFPR and WMSR to analyze large-scale data sets. To validate the performance of the proposed system, the world’s largest publicly accessible data set of wafer maps was built, comprising 811 457 real-world wafer maps. The experimental results show that the proposed features and overall system can process large-scale data sets effectively and efficiently, thereby meeting the requirements of current semiconductor fabrication.

Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

This paper provides a literature review on zero defect manufacturing based on the content analysis performed for 280 research articles published from 1987 to 2018 in a variety of academic journals ...

Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research

Wafer bin maps (WBM) in circuit probe (CP) tests that present specific defect patterns provide crucial information to identifying assignable causes in the semiconductor manufacturing process. However, most semiconductor companies rely on engineers using eyeball analysis to judge defect patterns, which is time-consuming and not reliable. Furthermore, the conventional statistical process control used in CP tests only monitors the mean or standard deviation of yield rates and failure percentages without detecting defect patterns. To fill the gap, this study aims to develop a manufacturing intelligence solution that integrates spatial statistics and neural networks for the detection and classification of WBM patterns to construct a system for online monitoring and visualisation of WBM failure percentages and corresponding spatial patterns with an extended statistical process control chart. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the effectiveness of the propos...

A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence

Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0

As semiconductor manufacturing reaching nanotechnology, to obtain high resolution and alignment accuracy via minimizing overlay errors within the tolerance is crucial. To address the needs of changing production and process conditions, this study aims to propose a novel dynamically adjusted proportional-integral (DAPI) run-to-run (R2R) controller to adapt equipment parameters to enhance the overlay control performance. This study evaluates the performance of controllers via the variation of each overlay factor and the variation of maximum overlay errors in real settings. To validate the effectiveness of the proposed approach, an empirical study was conducted in a leading semiconductor company in Taiwan and the results showed practical viability of the proposed DAPI controller to reduce overlay errors effectively than conventional exponentially weighted moving average controller used in this company.

Overlay Error Compensation Using Advanced Process Control With Dynamically Adjusted Proportional-Integral R2R Controller

Manufacturing intelligence for reducing false alarm of defect classification by integrating similarity matching approach in CMOS image sensor manufacturing

As high-speed computing is crucial to empower intelligent manufacturing for Industry 4.0, non-volatile memory (NVM) is critical semiconductor component of the cloud and data centre for the infrastr...

Ying-Jen Chen

Papers

A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence

Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0

Overlay Error Compensation Using Advanced Process Control With Dynamically Adjusted Proportional-Integral R2R Controller

Manufacturing intelligence for reducing false alarm of defect classification by integrating similarity matching approach in CMOS image sensor manufacturing

An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing