Semiconductor manufacturing analytics maturity: common barriers and methods to advance

Analyzing an operation’s current level of analytics maturity requires a thorough assessment of the hardware, analytics software and data management practices.

By Yuji Minegishi, General Manager, Gigaphoton Inc.

Imagine a manufacturing line in which every tool and every process, including the interactions among them, were being optimized automatically for precision, accuracy and speed.

What if you had a data clean room, where data pertaining to every process and tool in the production line was ready to use — standardized and clean — and you could quickly apply any artificial intelligence (AI) model to any data set across every process and tool? What if equipment could notify you when servicing is due, or alert you that a tool in the production line might fail before you complete your process?

This could be the fab of the future. More and more, advanced analytics and Industry 4.0 solutions are being recognized as the key to increased uptime and yield improvement in semiconductor manufacturing.

An analysis by McKinsey calls advanced analytics “the next leap forward in semiconductor yield improvement,” and a recent report by Deloitte 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. In fact, that same report noted that AI systems can analyze data thousands of times per minute — nearly 600 times the rate of human staff.