ABSTRACT
Currently, the semiconductor manufacturing industry is seeing rapid movement from 2D planar to 3D FinFET technology. Among SCE-enhanced scaled fin structures, depending on stress engineering to increase mobility, merged elevated source-drain (eSD) epi structures are widely used because they can maximize device performance by reducing Rsd. While there is active research on device and epi own defects related to eSD process, there is no study on yield effect. Smart manufacturing (SM) applications, which form the core of Industry 4.0, are difficult to find in bulk-FinFETs, and it is difficult to find hidden systematic defects of complex three-dimensional structures using limited analyses such as in-line monitoring and abnormal trend detection. In this study, we investigate the root-cause of gate to eSD short, which is the primary FinFET yield detractor, and we obtain an optimized solution to improve yield by 25.2% without performance degradation. These improvements are accomplished using our in-house SM platform that consists of four components: a virtual integration (VI) module for defining defects such as physical connection, void, and not open; a hot spot module for identifying the location of needed process control; an advanced analytics module including algorithms for selecting key features and predicting the fail bit; and an optimizer module that can co-optimize yield and performance.
ABSTRACT
The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed. The framework describes collaboration among internet of things (IoT), artificial intelligence, simulations, manufacturing execution systems, and advanced planning and scheduling systems. Subsequently, the implementation of the CPPS in actual plants is described. Temperature is a major factor that affects casting quality, and thus, temperature sensors and IoT communication devices were attached to casting machines. The well-known NoSQL database, HBase and the high-speed processing/analysis tool, Spark, are used for IoT repository and data pre-processing, respectively. Many machine learning algorithms such as decision tree, random forest, artificial neural network, and support vector machine were used for quality prediction and compared with R software. Finally, the operation of the entire system is demonstrated through a CPPS dashboard. In an era in which most CPPS-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks, use cases, usable software, and analytical methodologies. In addition, this study verifies the usefulness of CPPS by estimating quantitative effects. This is expected to contribute to the proliferation of CPPS in the industry.
