How many times per minute can your engineering staff check your manufacturing data? According to a report by Deloitte, you could increase your staff’s efficiency by 600 times with an AI solution, which can check data thousands of times in a minute. The same report notes that leading Japanese semiconductor manufacturers are already seeing improvements in productivity and yield from using AI to build real-time predictions about errors, equipment failures, and more.
Advanced semiconductor manufacturing analytics has been recognized as the next breakthrough to achieving significant yield improvement, but Industry 4.0 solutions take many forms. How can you be strategic about what you choose to invest in?
Our research has shown that barriers to achieving advanced manufacturing analytics maturity come from three primary sources:
1. Hardware
2. Analytics software
3. Data management
On the most elementary level, all semiconductor manufacturers can log, access, and manipulate basic equipment data that is stored in individual files (e.g., CSV, XML, etc.). Yet to be truly successful, modern chip production needs to leverage more sophisticated techniques to gather, analyze, and manage data.
This is where the power of AI truly shines: by developing advanced AI models, chip manufacturers can quickly trace failure points, optimize production, and ultimately increase yield.
We have developed the semiconductor manufacturing analytics maturity model above to help chip manufacturers think about how to resolve barriers related to hardware, analytics software, and data management. The end goal? To help manufacturers progress toward meaningful advanced predictive and prescriptive analytics.
To pinpoint where your operation lies on a maturity scale of 1-5, we’ve designed a short assessment. A series of questions will guide you to your score, break down what it means, and explore how Fabscape can help advance your maturity one level closer to being AI-optimized.
Are you ready to learn where your operation is on the framework?