Outlier Detection incorporates Parts Average Testing (PAT) and GDBN and is a vital enabler for chip companies serving the automotive industry. Our previous version of Outlier Detection was successful in several companies but was not flexible enough for varied types of parametric distributions. We also have outlier detection in place for aerospace customers but the requirements are slightly different. The new upgraded module for Outlier Detection is for release very soon and is for high-volume wafer sort. Several companies needing automotive capabilities later this year have already signed up for our new module.
WHAT IS OUTLIER DETECTION?
Outlier detection is a method of identifying outliers in data. When applied in yieldHUB, the system detects the anomalies using pre-defined recipes after the data has been uploaded to the cloud. This makes the solution highly scalable and puts the control of quality and reliability firmly in the hands of our customers instead of in the hands of the test houses.
What is an outlier?
In statistics, an outlier is a result that deviates from the normal values.
Why is outlier detection important?
Outliers pass the normal test program as there is nothing inherently wrong with them when compared with the datasheet. However, would you want a semiconductor that is very different from the norm controlling the airbags of your car, or inserted into your high end smart-phone? Don’t think so. So while the die would pass normal testing, algorithms are applied to remove these units so they never see the light of day.
|Outlier Detection: Key points|
|Large array of analysis for wafer sort, final test and WAT/PCM|
|SPAT, DPAT, GDBN|
|DPAT applied per wafer, per test, per site|
|Specialist tools also available or can be added.|
|No need to download data first, just typically: “search->select->analyse”.|
|Fabless and IDM company will be in control of the maps that are sent on to assembly|
|Puts you in control of quality and reliability|
|Module is fully Compliant with AEC-Q001|
|Goes beyond AEC-Q001|
|Algorithms for skewed data|
|Multiple spacial algorithms e.g. UPLY|
|Various input and output formats|
|Test limits for every site on every wafer recorded|
|Data can be stored indefinitely|
What industries need outlier detection?
Outlier detection is essential to the automotive industry. Quality and uniformity are essential to ensure safety in the running of motor vehicles.
Increasingly we are seeing the need for some form of outlier detection to be applied to dice going into consumer goods such as high-end smart-phones.
What’s different about our implementation?
The outlier detection algorithms will be easy to set-up per test and per product. The actual DPAT limits used for any die will be available in an audit trail. Who changed algorithms and when will be recorded. The effect on yield will be recorded and massively scalable analysis will be available like many other analysis tools in yieldHUB. You will also be able to run simulations easily on our web-based system before deciding on which algorithms to implement for a given test or bin.
What’s the next step?
If you’d like to find out more about Outlier Detection, register today. We will send you exclusive updates as soon as they are ready. You’ll be the first to hear about webinars and demos for the module.
Can yieldHUB Help You?
Contact yieldHUB today! Our global sales and support team will be happy to help.