Cluster tools are the key equipment in semiconductor manufacturing systems. They have been widely adopted for many wafer fabrication processes, such as chemical and physical vapor deposition processes. Reentrant wafer flows are commonly seen in cluster tool operations for deposition processes. It is very complicated to schedule cluster tools with reentrant processes. For a dual-arm cluster tool with two-time reentering, the existing studies point out that a one-wafer periodical (1-WP) schedule can be found, and it is optimal in terms of productivity. However, for some wafer fabrication processes, wafers should be processed at some PMs more than two times. This gives rise to a question of whether there still exists a 1-WP schedule for dual-arm cluster tools with the number of reentering times being more than two such that the cycle time of a tool can reach the lower bound. This problem is still open, and this is what this work wants to tackle. For a dual-arm cluster tool with the number of reentering times being k (>2) times, if there does not exist a value f ∈ {1, 2 …} such that k = 3f, theoretical proofs are given to show that a 1-WP schedule can be found, otherwise it does not exist. For cases with a 1-WP schedule, the cycle time can be obtained by analytical expressions. For the cases without a 1-WP schedule, two new methods for a three-wafer periodical schedule are proposed to improve the system productivity by comparing it with an existing three-wafer periodical schedule. The applications of the obtained results are demonstrated by examples. Wafer residency time constraints are required for some wafer fabrication processes. Note that the results obtained in this work cannot be directly applied to cluster tools with both reentrant wafer flows and wafer residency time constraints. Nevertheless, schedulablity and scheduling analyses for that applications can be conducted based on the obtained results in this work.

Semiconductor manufacturing relies on a long and complex production line for wafer fabrication. In a modern semiconductor fabrication plant (fab), a wafer manufacturing line is composed of more than 1,000 manufacturing tools and an overhead hoist system for delivering wafer lots among the tools. Novel technologies, such as Internet of Things (IoT) and software engineering technologies, are adopted in semiconductor manufacturing such that large amounts of data can be collected from manufacturing tools. Also, these tools and the overhead hoist system should cooperate well with each other. Due to the data-processing capacity limitation in a current fault detection and classification (FDC) system, the diagnosis efficiency is limited such that some undesired events cannot be detected in time, leading to significant economic loss. With the booming development of big data technology, this work conducts a study on a new FDC framework based on a Hadoop ecosystem to deal with the data-processing limitation and improve diagnosis efficiency. Also, a migration path is presented such that the current FDC system can be smoothly migrated to a Hadoop ecosystem-based one without shutting down a wafer fabrication line. Experimental results show that the proposed FDC framework can run safely and stably.

In modern semiconductor manufacturing, the computer-integrated manufacturing system plays an essential role in automation with plenty of software systems. Among them, the equipment automation program (EAP) is one of the fundamental systems to support the interconnection of various types of equipment. For the traditional EAP, the communication and logic models are tightly coupled. The occurrence of any exception in EAP may make the EAP power down such that no equipment is reachable. Additionally, it can handle a couple of manufacturing tools only. The extension of manufacturing tools in a semiconductor fab makes the investment in EAP unbearable. Thus, fabs are highly desired to solve such problems of the traditional EAP. To do so, this work designs a new framework for a distributed EAP system with new technologies being adopted to enhance the usage and stability of EAP. Additionally, this design philosophy makes the distributed EAP system more compatible and expansible. Further, this EAP system can be upgraded as communication and big data technologies advance. Experiments are carried out to verify the stability of the designed distributed EAP system.