Effective semiconductor yield management: 8 key questions
Learn how even small yield gains translate into major financial impact and why a robust yield information system is essential for fast, data driven decision making.
Effective yield management ensures that you minimise scrap and lower the cost of manufacturing. Whether you have one product or a thousand, the effort is worth it.
Take a product with a volume of 100 wafers per month where the yield is 93% and each wafer costs $5000 from the foundry.
A yield improvement to 96% means you will save $15,000 per month or $180,000 a year just on that product, while delivering the same number of good dice to your customers.
Large companies will see millions of dollars or savings per annum when an effective yield management strategy is employed. For this you need to have a suitable information system for yield.
Does your company have such a system?
If put on the spot, would you be able to answer the following questions for your top volume product in the next few minutes using your current yield management system?
- What is the yield up to today and what is the trend over the quarter?
- What are the highest failing bins and their trends?
- What are the highest failing tests and their trends?
- What impact is Fab having on the wafer sort and final test yields?
- Are the parameters being tested sensitive to the test software or hardware?
- Is the yield always uniform across the wafer? If not, why not?
- What percentage of good units are failed incorrectly because sites aren’t yielding consistently?
- Who is alerted if there are yield, site, bin or parametric issues in production and when and for what were the last alerts sent out?
If you and your engineers can answer these questions in minutes, then you are most of the way to effective Yield Analysis. If it would take you hours or days to get the information, then that is time that can be saved with a proper information system.
In a highly competitive landscape, why have your engineers waste hours and hours searching for data and generating reports before they can get cracking on finding root cause for yield loss?
Frequently asked questions
The practice of tracking, analyzing, and improving the proportion of good dice produced, so you minimize scrap and lower the cost of manufacturing. It applies whether you have one product or a thousand.
On a product running 100 wafers per month at $5,000 per wafer, moving from 93% to 96% yield saves $15,000 per month, or $180,000 a year, on that product alone. Large companies see millions per annum.
Eight, for your top volume product, in minutes: yield to date and quarterly trend, highest failing bins and their trends, highest failing tests and their trends, Fab's impact on wafer sort and final test yields, parameter sensitivity to test software and hardware, wafer-level yield uniformity, good units failed incorrectly through inconsistent site yielding, and who is alerted to yield, site, bin, or parametric issues and when.
Time it. If you and your engineers can answer those eight questions in minutes, you're most of the way to effective yield analysis. If it takes hours or days, that's time a proper information system gives back.
Sites that don't yield consistently fail good units incorrectly, so you scrap parts that were never defective.
Desktop tools mean engineers spend hours searching for data and generating reports before they can start on root cause. In a competitive landscape, that's the expensive part.
Wafer sort (probe) yield measures good dice on the wafer before packaging; final test yield measures good units after assembly. Tracking both matters because losses at final test can originate upstream in Fab or at sort, and you can only see that by correlating the two.
At minimum: wafer sort and final test data from every test house, Fab and WAT/PCM data, and the test program and hardware context behind each run. Centralizing and cleansing those in one database is what makes correlation across Fab, probe, and final test possible.
