First Published in EOS/ESD Technology Europe Spring 1990

Cost Benefit Analysis:
How Much is Enough?

Just how thorough does static-control have to be? Here's how to find
the cost-benefit ration of static-control efforts.

Richard Y. Moss
Hewlett-Packard Co.,
Palo Alto, CA, USA

Two of the questions most asked by manufacturing managers are "How good must my ESD-control program be?" and "How much money can it save me?" These are reasonable questions, considering that managers must justify their decisions in terms of return-on-investment or payback period.

Unfortunately, the first question is difficult to answer and the second almost impossible. One rarely knows the extent of ESD damage in a process until after it has been eliminated.

Basics

ESD is a statistically variable phenomenon. Subjecting a device or circuit to ESD pulses-- even in a controlled experiment using the best equipment-- produces wildly variable results. The voltage at which measurable damage occurs is unpredictable, as is the exact nature of the damage and the location of the discharge path. In addition, the damage can be hard to identify as ESD damage per se.

One way to deal with statistically variable phenomena is to use statistical methods. For example, if you drive an automobile, you know that rocks thrown up by tires of passing vehicles can randomly strike your windshield. Damage sometimes occurs and sometimes does not, depending on many variables. However, if you could dig into some insurance company files, you could calculate the average number of damaged windshields per 1,000 cars in different geographical areas. Here's how that might be done.

Probabilities

We'll call the average number of "hits" the "expected value," and use the symbol "y" to represent it. Using the Poison distribution, we can then calculate the probability of a certain number of hits, "r," in a particular stretch of road over a period of time. The equation looks like this:

P(r) is the probability of exactly r events, and y is the average number of occurrences of that event (Ref 1).

Assuming that ESD events are as random as the stones that hit your windshield, we can apply this formula to an ESD-control program and try to calculate the probability of zero ESD hits, or r = 0. The formula then becomes very simple:

P (0) = e^-y

The next step is to make this useful. First, suppose that y is the average number of electrostatic discharges in your facility or process without ESD control. This is a definition of how "bad" your environment is. Second, define factor "C," the percentage of ESD hits the control program prevents, which is a measure of its effectiveness. Finally, compute the factor "S," the percentage change in the probability of ESD damage. This is a measure of the savings a program generates. The resulting equation will look like this:

Fig 1 is a plot of this equation for various values of y-- the average number of ESD "hits" in the process. We can see that even in an ESD-free environment (where y < 0.1) we need an ESD-control program with C = 90% effectiveness to achieve a savings of S = 90% of ESD losses. In a situation where there is a high number of hits on the average (y = 20), a 99.5% effective program is needed to achieve 90% savings.

The Payoff

Even if we are not able to calculate the exact value of y, the message is clear. A half-hearted ESD-control effort won't do, especially in the face of conditions conducive to ESD events. Such conditions include low humidity, extensive use of plastic-packaging materials and state-of-the-art electronic components that are easily damaged.

The relationship between how much you spend on ESD control and how much you are able to save is a nonlinear one, and unless you produce a program that is more than 90% effective, you won't get the kind of savings you desire. But a person that takes ESD control seriously will find the rewards can be financially plentiful.

The Answer is Yes

Given the nonlinear relationship between the cost of ESD control and the savings that can result from it, some will ask whether it's worth the effort to establish the thorough control program that the statistics suggest is needed. The answer will almost always be yes, and to show why, here are the following examples. IC Manufacturing. Assembly-and-test yield of a high-speed, bipolar LSI device rose from 22% to 100% when conductive containers and operator wrist-strap grounders were adopted. Assembly-and-test yields losses of NMOS-LSI circuits were cut by 50%, and an 800% return-on-investment was realized in the first year of ESD control. Meanwhile, MOS-IC yield losses were reduced by 50% at final test through an ESD-control program. Electronics Manufacturing (Stores and Incoming). After implementation of an ESD-control program, the storage of bipolar transistor arrays in triboelectrically chargeable packaging was found to have caused 13% production failures and 1.5% field-warranty failures. Introduction of an ESD-control program at incoming test cut the reject rate for all low-power-Schottky-TTL ICs from 0.35% to 0.11%. Incoming inspection reject rates for linear ICs (op amps) fell from 2.6% to 0.26% when both vendors and users adopted ESD control. Electronics Manufacturing (Assembly and Test). Failure analysis showed that 60% to 80% of production rejects of CMOS logic may have been due to ESD damage. When an ESD-control program was adopted, 79% of beam-lead diode failures in hybrid circuitry disappeared. Reject rates on bipolar digital logic dropped from 0.18% to 0.11% after an ESD-control program was adopted in a computer-manufacturing area. Electronic Hardware (Field Reliability). Field-warranty failures of a microwave product dropped 30% in the first nine months with ESD-control. Warranty failure rate of a product using a large ratio of CMOS and low-power-Schottky-TTL ICs dropped 50% after a two-diode protection circuit was added to every input and output. An independent telephone system supplier experienced a 66% reduction in field-failure rate after service and installation personnel adopted ESD control.

References

1. C. Lipson and N.J. Sheth, Statistical Design and Analysis of Engineering Experiments, McGraw-Hill, 1973 (pp. 54-59).