The multiple demands of 3D NAND to enable yield and performance increase in difficulty at each generation. First generation devices, at 24-32 layer pairs, pushed process tools to extremes, going quickly from 10:1 to 40:1 aspect ratios for today’s 64-96 pair single tier devices. The aspect ratios increased as fast as the manufacturing challenges. To continue bit density scaling, processing improved to enable multi bit storage per layer, but still even more layer pairs are needed. With increasing layer pairs, plasma etch becomes exponentially slower.

This was quickly addressed by tier stacking—splitting the massive stack into two tiers—and it will likely increase to three or more tiers in the future. The advantage of a two-tier process is that it reduces the single etch step to a more manageable process, i.e., two 64 pair etches instead of one 128, or two 96 pair instead of one 192. 256 pair, two or three tier devices, are in development now, and 384 or more expected soon. The channel hole control improved in terms of individual profile, but at the cost of increasing device integration challenges, like adding a joint into the middle of the stack. These integration challenges are confounded by combining variation from multiple process steps. There is an increasing need to identify, measure, separate, and control each of these sources of variability.

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.

You may have noticed a newcomer to the 3D InCites community. But Onto Innovation is not a new company. It is the result of the 2019 merger of equals of two successful process control solutions providers who wanted to expand to serve the semiconductor manufacturing supply chain from end to end. I recently interviewed Mike Sheaffer, senior director of corporate communications for Onto Innovation, to get the story.