Common misconceptions about ADC
I find myself educating colleagues and customers alike about misconceptions surrounding the general field of ADC. Here are some classics:
- Automatic v. Automated Defect Classification: People frequently believe the “A” in ADC is for “automatic” and have a perception that an ADC system requires no human interaction whatsoever. The truth is that an ADC solution is no different from any other tool on the manufacturing floor. Just like an etcher or CMP system, ADC executes a recipe and produces a result. Also, like other tools, that recipe needs to be created by a tool owner and from time to time needs to be adjusted as processing changes are implemented.
- ADC is hard to configure: Setting up ADC classifier is like training your operator. Just as you would subject the human trainee to multiple examples of defects, ADC systems need a similar learning session. Again, like a human trainee you’d want to test their ability to learn and based on this test make minor adjustments if needed. Modern ADC solutions are built with an intuitive UI designed to guide you through the natural steps of collecting/managing samples, configuring image detection, setting up classifiers, and verifying the results. The biggest difference verses training a human is that you only need to train a single ADC system, not a small army of human reviewers.
- ADC classifier performance is unpredictable: A well represented set of samples, and clearly defined and visually different classes, is key to both ADC and operator. An ADC classifier is very predictable when that’s the case.
- ADC is perfect: Like a human operator, ADC is not perfect. If an operator is confused on certain samples, then ADC will most likely be, too.
