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Knowledge Base/DOE in Semiconductor Manufacturing Process: Screening Strategy for Numerous Factors
Semiconductor Process6 min read

DOE in Semiconductor Manufacturing Process: Screening Strategy for Numerous Factors

Do you often encounter problems with new product introduction where yield consistently stalls? This article is highly relevant! The author shares a painful past experience where a CPK report led to a room-wide silence. He discusses why traditional experimental methods are ineffective when processes involve numerous parameters, especially given complex "interaction effects" among factors. After reading this, you will learn how to more intelligently screen factors, identify the true root cause of issues, and avoid 'groping in the dark'.

# That Day, the CPK Report Came Out, and the Whole Room Fell Silent for Three Seconds

I still remember several years ago, when we introduced a new product. Ostensibly, the process flow was established, and yield should have been stable. However, during a weekly meeting one day, the PM suddenly showed a CPK report with a grim face. A critical parameter's CPK was only 1.08, and DPMO soared directly to 6210. The entire conference room went quiet instantly; you could hear a pin drop. The boss then asked, "Have you guys actually done the process DOE? Have the factors been screened?" To be honest, I was murmuring to myself then, with so many factors, how do you even screen them?

What's the Problem?

You must have encountered this situation too: in a single process, there are easily dozens of parameters that can be adjusted. Oven temperature, time, gas flow, power, RPM... just combining a few, the number of experiments explodes. If every one were done as a Full Factorial, the product might be off the market just by the time the experiments are finished. Moreover, many times there are "interaction effects" among these factors; that is to say, the effect of parameter A changes depending on parameter B. You might not see it by looking at a single factor, but once combined, problems arise.

So the key is, we must identify those factors that genuinely have an impact, and an impact that is "significant enough." Those with small or no impact can be directly ignored. This is the importance of "factor screening" in DOE (Design of Experiments).

How Is It Actually Done?

The simplest and most commonly used is "Fractional Factorial Design." Frankly speaking, it's a "clever shortcut." It doesn't run through all possible combinations; instead, it selects only a portion of them for experiments.

For example, suppose you have 7 process factors, with each factor set at two levels (high and low). If you were to do a Full Factorial, you would need to run 2 to the power of 7, which is 128 experimental runs. Just thinking about it makes your scalp tingle. However, if we use a 2^(7-4) Fractional Factorial design, it only requires 16 experimental runs to help you screen out which factors are important.

In other words, with a small number of experiments, you can determine which factors are potential contributors to "main effects" or "major interaction effects." Of course, there's a trade-off: you might sacrifice some resolution for higher-order interaction effects. But frankly, in the early stages of process development, quickly identifying critical factors is far more important than getting bogged down by third- or fourth-order interaction effects.

Common Pitfalls

I once saw a rookie engineer who received a 2^(7-4) experimental design table and felt it was very easy, as it only required 16 runs. As a result, he set all factors to "high level" or "low level" extreme values, thinking that this would make the effects more obvious. What was the outcome? Many parameters exceeded the machine's operating range, causing the equipment to fail to run, or the product was directly scrapped.

So the key is, factor level setting is crucial! Don't just set "high" to an extreme value; instead, it should be set within a "meaningful" and "operable" range. For example, if the oven temperature is set at 200 degrees, you cannot just set "high" to 300 degrees because the product might be directly burned. It should be set within a range like 190 degrees and 210 degrees, where you believe there might be a difference, but it won't directly ruin the product.

Another pitfall is, after getting the results, rushing to make adjustments just because a small P-value is observed. However, sometimes, even if the P-value is small, the Effect Size is very minor, meaning that while it is statistically significant, the actual improvement is negligible. At this point, the engineer's experienced judgment becomes very important.

One Thing You Can Do Today

Take out a list of process factors that need optimization from your hand, and consider which factors are "truly" within your controllable range.

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