Building a fab data platform

Demystifying Data in Semiconductor Manufacturing with Fabscape

In the intricate and ever-evolving world of semiconductor manufacturing, the ability to seamlessly collect, analyze, and visualize data is more crucial than ever. Fabscape, an open platform designed for the semiconductor industry, stands out by offering these capabilities in a customizable and collaborative environment. My experience with Fabscape, shared during a live demo with Semiconductor Digest in March 2023, highlights the platform’s utility and my role in enhancing its data capabilities.

The Genesis of Our Fabscape Demo

Alongside Yuji Minegishi from Gigaphoton, I demonstrated how semiconductor developers could transform a blank slate into a dynamic data visualization tool using Fabscape. This platform isn’t just another software suite; it’s a robust foundation for innovation in data management within the semiconductor sector.

Building Blocks of Fabscape

Fabscape is built on a modular structure that supports extensive customization through plugins and drivers. These components are essential for tailoring the platform to meet the specific needs of device manufacturers and equipment vendors:

• Plugins: These add functionalities to Fabscape, allowing for specialized data visualization. Without plugins, Fabscape would be merely an empty shell.
• Drivers: These are crucial for data acquisition, tasked with collecting data directly from equipment and making it accessible to Fabscape’s backend. Like plugins, drivers are deployed as Docker containers, simplifying development and deployment and making individual services (including ML services) modular.

My Role in the Digital Transformation Department

As a member of the Digital Transformation Department at Gigaphoton, I contributed to enhancing the data capabilities of Fabscape, particularly in the realm of machine learning. I developed several key plugins for Gigaphoton equipment that enabled advanced ML model functionalities, aligning with the strict security, hardware, and communication standards of the semiconductor manufacturing environment. My focus was on designing plugins that facilitate the ingestion, storage, and processing of data, as well as serving sophisticated machine learning models. This included implementing architectures that support the seamless integration of these models into Fabscape, and setting up systems to monitor their performance and facilitate ongoing development.

From Data Collection to Predictive Analytics

During the demo, I created a driver to collect data—not from semiconductor equipment but from a public API providing COVID statistics. This example illustrated how Fabscape could be adapted for various data sources. We chose gRPC for the protocol to fetch data due to its high performance and efficiency in low-latency, high-throughput scenarios, which are crucial in semiconductor manufacturing environments. Additionally, gRPC’s strong type-safety, straightforward IDL (Interface Definition Language), and support for multiple programming environments make it an ideal choice for our scalable and interoperable system. I then integrated a user interface that leveraged reusable code components to display this data effectively.

Further extending Fabscape’s capabilities, I showcased how to integrate AI and machine learning for predictive analytics. Using a custom Jupyter Notebook plugin developed for Fabscape, we were able to pull equipment parameter data, apply machine learning models, and perform predictive analytics to forecast equipment behavior.

Why Fabscape?

Fabscape’s architecture promotes a collaborative approach, crucial for tackling complex challenges in semiconductor manufacturing. Its ability to be customized with proprietary and secure data solutions allows organizations to maintain data privacy while benefiting from shared innovations.

Engage with Fabscape

For those interested in exploring the potential of Fabscape, the platform offers a free Toolkit for developers. For more tailored solutions, Gigaphoton’s Advisory Program provides an opportunity to work directly with experts like myself to refine your data strategy and enhance your manufacturing processes.

Conclusion

The full capabilities of Fabscape were on display during the Semiconductor Digest webinar, which is available on demand. This event was not only a demonstration of technology but also a testament to the collaborative and innovative spirit that drives the semiconductor industry forward, and my role as a data scientist and engineer in shaping this future.

I invite you to watch the full recording to appreciate the depth of the solutions provided and to understand how Fabscape could revolutionize data management in your fab operations.

Being a good data scientist is more than being able to create an ad-hoc model on a curated data set. To bring true value with ML means planning and implementing the architecture to ingest, store, process data, serve the model, monitor, and facilitate ongoing development.

Working in the Digital Transformation Department of Gigaphoton, I contributed to a flexible open data platform to serve the strict security, hardware, and communication standards of the environment of the semiconductor Fab.