Semiconductor wafer manufacturing is a long and complex process comprising of several process steps. Normally it takes somewhere between five weeks to several months to manufacture a fully functional IC that can be shipped to the customers. During the manufacturing phase, each process node generates parametric and process data to help monitor and evaluate operational efficiency and health of the wafer being processed.

The process of data collection, storage, and analysis poses a significant challenge for many systems. This difficulty arises due to the sheer volume of electrical, mechanical, and other parameters being measured for each individual chip. The statistical analysis becomes even more difficult if we take into consideration the huge data sizes containing thousands of ICs being fabricated on a few lots and thousands of lots per product.

Semiconductor wafer manufacturing

Boosting Productivity: Streamlining Data Collection for Yield Analysis

The Product Engineering Team has to spend a considerable amount of time just to collect, store and clean the data that can be used for statistical modelling and analysis. This situation diverts their attention from high-value tasks to lower-value activities, such as collecting raw data. The data collection often involves multiple sources, including the process data from the Manufacturing Execution System (MES). It is imperative to document and monitor the process and parametric data at every node as some yield loss might happen at any step as the wafer progresses from one step to another.

The role of product engineers becomes crucial as they are tasked with conducting intricate statistical and yield analyses. Their objective is to accurately identify the root cause of defects that affect yield and disrupt the production schedule. Time spent by the engineering team on data collection, cleaning, and analysis can detract from other vital activities. For instance, it can limit their ability to incorporate customer feedback into the product and make necessary adjustments to the design and production cycle. Also, this situation could impede the transition of products from the sampling stage to high volume production. Lastly, it may compromise the team’s capacity to ensure cost-effective and timely production of the products.

Product yield is no doubt an important KPI of product engineering team but how to make sure that they have enough time to focus on other high value activities too?

Enhancing Engineering Productivity via Automated Data Management

A complete end-to-end yield management solution (YMS) like yieldWerx. The product engineers focus on high value activities and perform the data cleansing and management functions automatically in the background. The tool can be connected directly to the MES and data can be loaded to its cloud servers either in real time or scheduled for specific time intervals. The YMS then automatically maps the raw data to its corresponding wafer, lot, facility and develops the associated genealogy and stores it in a central database for analysis. The product engineer can then perform required analysis and generate reports with the help of simple clicks and the intuitive user interface of yieldWerx.

One of our customers, QuickLogic, has reported a remarkable 90% reduction in their product engineering time. This significant efficiency boost is due to yieldWerx’s ability to eliminate noise from data sets, enabling rapid analysis and thereby enhancing overall productivity. Thanks to yieldWerx’s powerful data management capabilities, engineers can now analyze characterization data in as little as 30 minutes. Additionally, they can swiftly prepare a comprehensive dashboard of reports. These advancements facilitate quick, enterprise-wide decision making, streamlining processes significantly.

If you are interested in how, you can get more out of your product engineering team without compromising on quality and yield, contact us today and schedule a demo. Our sales and technical team will get in touch with you to highlight the increased productivity that your team can achieve by deploying yieldWerx.