# Accelerate time-to-market by saving ESD test time

Ideally, if the sampled ESD readings
are bounded from above and below, with a gap between the smallest
readings and the LSL, then one can make inferences regarding the pins
that were not sampled. Statistical confidence statements can be made
with regard to the proportion that is likely to fall below the LSL among
the pins that were not sampled.

An applied sampling method has
to be simple and practical. First, the failure distribution of the clone
pins should be Gaussian in nature with a reasonably tight range (R) as
determined by the distance between the maximum voltage where all pins
still pass (V1), and minimum voltage where all pins fail (V2). With
this value of R, the Sigma (σ) for the distribution can be estimated.

But
how many data readings are necessary? From known statistics for normal
distribution, the expected value of the range is a multiple of σ with
the multiple as a function of the count of readings. It turns out that
30 is a practical count for which the range R would be within about four
sigmas (**Fig. 1**). For distributions that are Gaussian in nature,
the Min voltage and the mid-range voltage where half of the pins fail
(VM) can be used with equal accuracy (**Fig. 2**). Find the V1 and VM
values by simply selecting 30 random clones and testing until half of
them fail. The number of sigmas that can fit between LSL and VM
determines the cumulative distribution function (CDF) at the LSL (**Fig. 3**). The CDF at the LSL represents the proportion out of the specification for each sampled pin.

The
final step is to extend the probability to insure that all the
unmeasured pins still are above LSL with a confidence level of 99
percent. In summary, this approach enables information collected on just
30 identical pins to be used to determine the required sampling number
with a confidence level of 99 percent such that all cloned pins are
validly represented by our ESD test sample.

**ESD sampling**

To
use the proposed method, failure data has to be collected on the
identical clones sample with stepped voltages until half of them fail.
For the cloned IO pins, their ESD failure distributions will follow a
common behavior since the same protection device governs their failure
levels. Some variations are expected due to physical differences in the
IC layout and the failure thresholds derived by thermal limits due to
process effects. Sampling is applied once conditions are met.

To
illustrate, suppose there are N clones and 30 are selected to measure
the maximum voltage point where none of them fail (V1) and the voltage
point where half of the 30 pins fail (VM). The range is estimated as
twice the difference between VM and V1. The range and the distance from
LSL to VM can be applied as described above to find the required
sampling, n, for a confidence level of (1-α). Application of this method
has shown that if V1 is 2X of LSL, only about 10% of the clones need to
be used for ESD testing (**Fig.4**).

**Click on image to enlarge.****Figure 4: Predicted sampling curves for 300 cloned IOs. The required sampling as a function of distance from LSL to V1 for distributions with different ranges. The method becomes more viable and practical with tighter distribution (the curves shown on the left).**

In general, the farther away V1 is from LSL and the tighter the range, the better the benefit from sampling (

**Fig. 4**). Significant test time savings can be achieved for IC products comprised of hundreds of clones without compromising ESD test accuracy.

**Test time savings**

The reason to sample is to save time and money. But even more important is the savings in qualification time and improving time to market. For example, it is often the case that when large numbers of identical pins are tested for ESD, the spurious variations that can result in the data cannot be replicated a second time or a third time. The probability for this to occur increases as the pin count increases, so the first step in intelligent testing is to remove these uncertainties and focus on the real issues.

**Interdisciplinary efforts and the future**

This new ESD sampling, presented at the 2012 ESD Symposium, was developed by a joint committee of ESD Association Standards representing several major IC suppliers and included inputs from people with engineering and mathematical backgrounds. The collaboration among various disciplines is an example of the path necessary to take in order to succeed in the increasingly competitive semiconductor industry landscape. There is a need for interdisciplinary scientific development similar to the paradigm existing among departments at most major universities. The Joint Electron Devices Engineering Council and the ESD Association Standards have preliminarily accepted the intelligent sampling method, pending an official documentation for ballot approval.

**References**

[1] Grant and Leavenworth, Statistical Quality Control, McGraw Hill Series.

[2]. C. Duvvury, J. Dobson, R. Gauthier, E. Grund, B. Carn, W. Stadler, J. Miller, T. Welsher, R. Gaertner, S. Ward, M. Chaine, A. Righter, “Sampling Pin Approaches for ESD Applications,” Presented at the EOS/ESD Symposium, September 12-14, 2012, Tucson, AZ.

**About the authors**

**Charvaka Duvvury**is a Texas Instruments Fellow and an IEEE Fellow, working in the Advanced CMOS Technology Development. He is also a member of the Board of Directors for the ESD Association since 1997. His current work is on development and company wide support on ESD for the nanometer submicron CMOS technologies. Charvaka is co-founder and co-chair of the Industry Council on ESD Target Levels whose mission is to establish safe and realistic component ESD target levels while meeting the silicon technology challenges.

**Joel Dobson**has been working at Texas Instruments for 21 years where he is a Distinguished Member of the Technical Staff. He is currently working as a corporate statistics expert with specializations semiconductor reliability, quality control and statistical modeling. Dobson is an Accredited Professional Statistician of the American Statistical Association and certified as a Quality Engineer, a Six Sigma Green Belt, and a Six Sigma Black Belt from the American Society of Quality.