Process Watch: Know your enemy

By Douglas G. Sutherland and David W. Price

Author’s Note: This is the fifth in a series of 10 installments exploring the fundamental truths about process control—defect inspection and metrology—for the semiconductor industry. Each article in this series introduces one of the 10 fundamental truths and highlights their implications.

In the last installment we discussed the idea that uncertainty in measurement is part of the process. Anything that degrades the quality of the measurement also degrades the quality of the process because it introduces more variability into the Statistical Process Control (SPC) charts which are windows into the health of the process. In this paper we will expand upon those ideas.

The fifth fundamental truth of process control for the semiconductor IC industry is:

Variability is the Enemy of a Well Controlled Process.

In a wafer fab there are many different types of variability — all of them are bad.

  • Variability in the lot arrival rate, the processing time and the downtime of processing tools, to name just a few sources, all contribute to increased cycle time
  • Variability in the physical features (CD, film thickness, side-wall angle, etc.) contribute to increased leakage current, slower part speed, and yield loss
  • Variability in the defect rate leads to variability in the final yield, in the infant mortality rate, and in long-term reliability
  • Most importantly, variability degrades our ability to monitor small changes in the process – the signal must be greater than the noise in order to be detectable

There is nearly always some way to adjust the average of a given measurement, but the range of values is much harder to control and often much more important. For example, if a man has his feet in an oven and his head in a freezer, his average body temperature may well be 98o F but that fact won’t make him any less dead. Variability kills, and any effort to reduce it is usually time and money well spent.

Variability in Defect Inspection

Figure 1 below shows two simulated SPC charts that monitor the defect count at a given process step. Each chart samples every fifth lot (20 percent lot sampling). Both charts have an excursion at lot number 300 where a defect of interest (DOI) that makes up 10 percent of the total suddenly increases by three-fold. In the left chart the excursion would be caught within 8.5 lots on average, but in the right chart the same excursion would not be caught, on average, until 38.6 lots passed. The only difference is that the chart on the right has twice as much variability.

In general, for an excursion to be caught in a timely fashion it must be large enough to increase the average total defect count by an amount equivalent to three standard deviations of the baseline. If the baseline defect count is very noisy (high variability) then only large excursions will be detectable. Often people think this is the purpose of excursion monitoring: to find the big changes in defectivity. It is not.

KLAT_figures_web_Figure 1 (left) KLAT_figures_web_Figure 1 (right)

 

Figure 1. Two identical SPC charts showing the defect count at a given step but the chart on the left has half the variability of the chart on the right. The excursion at lot number 300 is detected on the left chart within 8.5 lots (on average) but the same excursion is not detected for 38.6 lots on the chart on the right. Increasing the variability by 2x increases the exposed lots by over 4.5x 

In our experience it is nearly always the smaller excursions that cause the most damage simply because they go undetected for prolonged periods of time. The big excursions get a lot of attention and generate a lot of activity but the dollar value of their impact is usually quite small in comparison. It is not uncommon to see low-level excursions cause upwards of $30,000,000 in yield loss. Large excursions are usually identified very quickly and usually result in a few million dollars of loss.

Other sources of variability in inspection data are low capture rate (CR) and poor CR stability. Defect inspection tools that have low CR will inherently have low CR stability. This means that even if the exact same defects could be moved to a different wafer you would not get the same result because of the different background signal from one wafer to the next. This adds significant variability into the SPC chart and can severely impair the ability to detect changes in the defect level.

It’s similar to looking at the stars on two different nights. Sometimes you see them all; sometimes you don’t. The stars are still there—it’s just that the conditions have changed. Something analogous happens with wafers. The exact same defects may be present but the conditions (film stack, CD, overlay, etc.) have changed. An inspection tool with a tunable wavelength allows you to filter out the background noise in the same way that a radio telescope allows you to see through the clouds. Inspection tools with flexible optical parameter settings (wavelength, aperture, polarization, etc.) produce robust inspections that effectively handle changes in background noise and take the variability out of the defect inspection process.

Variability in Metrology

Figure 2 shows two different distributions of critical dimension (CD). The chart of the left shows a distribution that spans the full range from the lower control limit (LCL) all the way to the upper control limit (UCL). Any change in the position of the average will result in some part of the tail extending beyond the one of the control limits.

KLAT_figures_web_Figure 2 (left) KLAT_figures_web_Figure 2 (right)

 

Figure 2. The distribution of CD values. The left chart shows a highly variable process and the right chart shows a process that has low variability.

The right hand chart has much less variability. Not only can the average value change a bit in either direction but there is enough room that one may deliberately choose to shift the position of the center point. Depending on the step this may allow one to tune the speed of the part or make trade-offs between part speed and leakage current.

Up to 10 percent of the breadth of these distributions comes from the CD tool used to measure the value in the first place. Contributions to the variability—total measurement uncertainty (TMU) —come from static precision, dynamic precision, long-term stability and matching. Clearly, metrology tools that have better TMU allow more latitude in the fine tuning of process control. This becomes especially important when using feed forward and/or feedback loops that can compound noise in the measurement process.

Obviously the best way to reduce variability is with the process itself. However, process control tools (inspection and metrology) and process control strategies can contribute to that variability in meaningful ways if they are poorly implemented. Metrology and inspection are the windows into your process: they allow you to see what parts of the process are stable, and more importantly, what parts are changing. The expense of implementing a superior process control strategy is nearly always recouped in terms of reducing variability and making the measurements more sensitive to small changes that can cause the most financial damage.

About the authors:

Dr. David W. Price is a Senior Director at KLA-Tencor Corp. Dr. Douglas Sutherland is a Principal Scientist at KLA-Tencor Corp. Over the last 10 years, Dr. Price and Dr. Sutherland have worked directly with more than 50 semiconductor IC manufacturers to help them optimize their overall inspection strategy to achieve the lowest total cost. This series of articles attempts to summarize some of the universal lessons they have observed through these engagements.

Read more Process Watch:

The most expensive defect

Process Watch: Fab managers don’t like surprises

Process Watch: The 10 fundamental truths of process control for the semiconductor IC industry

Process Watch: Exploring the dark side

The Dangerous Disappearing Defect,” “Skewing the Defect Pareto,” “Bigger and Better Wafers,” “Taming the Overlay Beast,” “A Clean, Well-Lighted Reticle,” “Breaking Parametric Correlation,” “Cycle Time’s Paradoxical Relationship to Yield,” and “The Gleam of Well-Polished Sapphire.”

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One thought on “Process Watch: Know your enemy

  1. Michael Clayton

    Thanks for the complex example with its many sources of variation for FT5!
    Good pitch for better metrology tools. Links to Variance Components modeling for nested and crossed data models that you used in your customer support would be interesting. Not a simple topic but of course the “fundamental truth” is elegantly simple:
    “Variability is the Enemy of a Well Controlled Process”
    Funny that Taguchi taught focus on variability so well, and most of our quality gurus long ago focused mostly on shifts in the mean of a process. He was Professor of EE, while our quality gurus were statisticians mostly. They went to war on Taguchi for his “misuse” of stats, sadly, missing the key points for a while.

    Reply

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