With the recent global COVID-19 pandemic, the world has learnt how to adjust business and manufacturing operations to compensate for the need to isolate and quarantine. This has resulted in companies working remotely to support facilities that are running with minimal staff.

The adoption of Industry 4.0 methodologies merges well with the need to reduce social contact inside fabs. As more decisions become automated and factory systems become more interconnected, more employees can monitor and control operations from home. This means that factory output can remain high while minimizing the risk of having key employees succumbing to COVID-19.

The first step toward developing highly integrated Smart factories is the deployment of a real time Digital Twin. A Digital Twin is a living data warehouse that is not only a repository of factory state information, but it is also a system that uses the collected data from disparate factory systems to derive new information. This information is used to connect and control systems throughout the factory with the express purpose of making more accurate real time predictions of expected factory outcomes. Using the Digital Twin, WIP Scheduling, Location Tracking and Delivery, Maintenance Management, and Process Control systems can make automatic optimal decisions and reduce the need for humans to intercede.

Digital Twin and Applications

Multiple applications are built upon a Digital Twin, the primary highest ROI application is a Factory Scheduler. This system makes key decisions about what material to move where and when. A well-tuned Factory Scheduler can be adjusted to optimize WIP movement and tool loadings. This minimizes the number of people on the factory floor by eliminating inefficiencies in operations and dramatically reduces the need for real time operator-based decision making on the production floor. WIP Location Tracking and a Smart Dispatch are important extensions to the Scheduler. By employing a Smart WIP Tracking and Dispatch system, the physical delivery of material can be managed to minimize contact with other operators. Maximizing the number of lots moved at a time and minimizing the number of stops on a delivery route can reduce social contact. In sites with automated material delivery systems, a Smart Scheduler and Dispatch system work with the delivery system to move the material at just the right time and reduce the need for intervention. Additionally, using a Smart Digital Twin enabled system, factories can enhance floor staff contact tracing by combining material location tracking and operator transactions.

Another key set of components that benefit from a Smart Digital Twin are the Process Control (FDC and APC), Metrology Sampling, and the Maintenance Systems. By utilizing predictive systems, equipment downtime can be reduced. By understanding the health of the equipment, Smart integrated Process Control systems can maximize uptime by make process adjustments to compensate for changes in equipment performance. Connecting the information from the Fault Detection System with a Metrology Sampling System enables the metrology sampler to minimize the amount of sampled material and focus on at risk lots. This reduces the number of operators and decision makers required to manage the metrology queue. Those systems also provide predictive capabilities for excursions and changes in equipment health. This allows factories to better schedule preventative maintenance and manage changes in tool health that minimize the need for maintenance and equipment engineering personnel to be in the factory.

Metrology Sampling Dashboard

FDC System Example

While independently these systems can provide benefit to semiconductor operations, the combination of WIP and metrology Scheduling, Location Tracking and Dispatch, Process Control and maintenance systems with a comprehensive Digital Twin, multiplies the benefit. This is done by providing interconnection between the systems, allowing them to easily share data, and developing a connected environment that enables automatic predictive systems to make intelligent decisions about factory operations.