Problems we solve for transport and automotive technology
Automatic detection of outliers via AECQ001 is critical and we support advanced algorithms also that the customer owns.
Characterisation ensures that variation in parametric behaviour due to allowed changes in Material or Test Conditions is understood early.
Burn-in and Life Test: Drift analysis
The analysis of population drift and individual die drift through burn-in and life test are very important and fully supported by yieldHUB.
Multiple steps to test parts makes manual cleansing of the data difficult. yieldHUB automates this, even combining data from dozens of logs.
yieldHUB supports traceability and fast analysis of returns.
Sometimes a die (or part) needs thorough analysis across all tests, including how the tests compare. This capability is available in yieldHUB.
“Outliers: parts whose parameters are statistically different from the typical part" AEC
Outlier detection is a method of identifying a member in the group that deviates grossly from the norm. You test semiconductors across different factors, e.g. voltage, current & compare the performance.
Burn-in and Drift Analysis
When you collect data at each step of burn-in or life test, you need to see how key tests drifted.
The ‘population’ drift is as important as the drift of any individual die. After collecting the data, you don’t want to have to take more than a few minutes setting up before you see the results.
If you use fuses on your die to somehow encode lot id, wafer id and x/y then yieldHUB can use this information to make the die searchable in the database. If then there is a customer return, it will be possible to analyse how the die behaved for any tests it underwent in manufacturing.
Frequently Asked Questions
Is there a limit on the number of tests you support?
We have customers with 20000 tests in a datalog. We have yet to see an upper limit on how many tests the yieldHUB systems can support.
Do you support OTP?
Yes, we support OTP/Fuse ID for traceability.
Which data formats do you support?
We have yet to receive a data format we cannot support. Most common is STDF, but any type of text or binary format can be supported once it’s consistent.
What type of algorithms do you support for outlier detection?
AECQ001 provides algorithms that we implement in yieldHUB. In the same way, a customer can write scripts for even more advanced methods and we will support these without needing to know or see the inner workings.
The automotive industry has exacting data management requirements. We need to store our data for many years after the end of life of a product. Having a cloud-based, secure system is ideal for us. We started looking at yield management tools available on the market and found yieldHUB an excellent solution. It is cost-effective and easy to use. We find the system to be very intuitive. The options are categories that you’d want to see are quite intuitive. You can tell that it was developed by people in the industry, who know what they are doing.Patrick McNamee, Director of Operations, EnSilica