Kimball deterministic polishing istfa 2009

ISTFA™ 2009, Conference Proceedings from the 35th International Symposium for Testing and Failure Analysis November 15–19, 2009, San Jose, California, USA, p 130-134

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Deterministic Polishing Applications in Failure Analysis Mark Kimball Principal Member of Technical Staff Maxim Integrated Products 7250 Evergreen Parkway Hillsboro, OR 97124

fashion underneath a polishing tool, and planarity problems are common to all types of polishing systems.

Abstract Development of a reliable mechanical decapsulation procedure for an IC process incorporating Cu and organic passivation layers resulted in a better understanding of the polishing process. The improved polishing technique --Deterministic Polishing-- is an adaptation of an advanced polishing technology employed by the optics industry to fabricate highly accurate optical elements. These results can be applied to a broad class of polishing applications

Nonuniform Polishing Problems Polishing tools that raster the sample underneath a rotating polishing tip can exhibit nonuniform polishing. This is particularly true for tools that use a mechanically rastered XY table, where the “slow” axis is driven by a reduction gear connected to the “fast” axis. In this type of tool, the tool traces out a Lissajous figure. See Figure 1.

Introduction Devices fabricated with copper metallization can present significant problems with regard to failure analysis, particularly when it is necessary to decapsulate them. Acids used to perform chemical decapsulation can attack copper circuit metallization and organic low-K dielectrics.

Mechanical decapsulation can work, but we have found that sample preparation issues and inherent limitations in the polishing process can lead to less than optimum results.

Investigations into the polishing problem, with emphasis on rastered (XY) polishing tools (such as the ASAP-1) were performed.

Theoretical modeling using a variation on Preston’s Equation was used to help optimize the polishing process. Deterministic Polishing, a technique derived from the Optics industry, was investigated as a way to address some of these problems.

Theoretical and real-world results have shown that Deterministic Polishing (DP) can give improved polishing results. These improvements are not confined to copper or low-K devices. Guidelines for achieving optimal results are provided, some of which are also applicable to currently-available (non-DP-based) tools.

Figure 1 Lissajous pattern drawn by the ASAP. This was traced out by placing a pencil lead in the tool while its XY table was in motion. The sinusoidal table motion leads to repetitive variations in the tool step size and dwell time, which, in turn, results in nonuniform polishing.

Polishing Issues

Since the table motion is sinusoidal, in addition to variations in the effective coverage of the polishing bit, the velocity of the table varies across the sample: it moves fastest near the center and slowest at the edges.

Polishing problems can be separated into two categories: nonuniform polishing effects, and planarity problems due to sample mounting and tool setup. The former problem is most apparent on polishing systems that raster the sample in an X-Y 130

Another effect common to all types of rastered polishing systems can impact the quality of the polish. As the tool approaches the edge of the sample, its contact area decreases.

As a result, the polishing pressure increases. This also causes nonuniform polishing.

Sample Planarity Problems In our experience, it is very difficult to achieve perfect sample planarity. In the case of performing parallel lap on packaged devices, we have observed that dice are not assembled exactly parallel to the external plane of the package. This is most likely due to thickness variations in the die attach material. It also is difficult to mount parts on sample holders and get them exactly flat. For relatively large devices, a tilt of just a few microns across the span of a die can result in the exposure or removal of different metal layers across the die, so this can be a significant problem. Many polishing tools have adjustments which are designed to address this problem, but in our experience it is difficult to exactly compensate for sample tilt.

Some tools have an optical alignment system that can improve the situation, but they require additional sample preparation; or that system may not be available or applicable to a particular sample.

Figure 2. Polishing uniformity vs. position, using mechanical XY table.

In this simulation, the polishing uniformity of a single die is shown. The peaks at each corner of the sample are caused by variations in pressure as the polishing tool moves over the corners of the die: pressure increases as the contact area decreases. Also, note the higher-frequency variations across the die . This is a consequence of the nonuniform sinusoidal motion of the mechanical XY table.

We began addressing these problems by modeling the polishing process.

Modeling the Polishing Process Although many theoretical approaches have been taken to model the polishing process, in most cases they reduce to variations on Preston’s Equation [1]. Time integration of Preston’s equation results in

We have found that Deterministic Polishing can eliminate, or at least improve, polishing artifacts caused by these problems.

T(x,y) = K*V(x,y)*P(x,y)*t(x,y)

Deterministic Polishing is a technique used in the optics industry to produce highly accurate optical surfaces [2, 3]. In its typical application a small polishing tool is used to interactively finish a larger glass surface. Deviations from the desired shape are periodically measured, fed into a computer, and a special program controls the tool. The tool spends more time over the highest regions and thus selectively polishes the surface until the desired shape is achieved. This approach has yielded surfaces that can deviate from the desired shape by only a few nanometers [2].

Deterministic Polishing

(Eq’n 1)

Where T(x,y) = Thickness of material removed at each x,y point.

K = A polishing constant, dependant on the materials present.

V(x,y) = Velocity of the polishing medium at each sample point.

P(x,y) = Pressure between the sample and polishing medium over each sample point. t(x,y) = Elapsed time over each sample point.

Computer simulations of samples polished using a uniform velocity have indicated that DP can produce good results -with some caveats. See Figure 3.

In this derivation, it is assumed that T(x,y), V(x,y) and P(x,y) are independent of time.

It can be seen that nonuniform velocity V(x,y) and variable dwell time t(x,y) results in a nonuniform polishing rate. In addition, pressure variations (which occur when the tool approaches the edges of the sample) also contribute to polishing nonuniformity.

A computer program was written to simulate polishing using this equation. See Figure 2 for an example of the program output.

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material that is added later (for instance, a 2-part epoxy).

Mechanical decapsulation of acid-sensitive devices would naturally result in edge protection. The expected result of adding edge protection was to eliminate pressure variations near the edge of the die and therefore improve polish uniformity. The dwell-time problem can be addressed by ensuring that the edge protection “zone” is wide enough for the polishing tool to travel all the way across the sample, rather than reversing at the sample edges. If edge protection is used, along with extending the tool motion, the result can be very good. See Figure 4. Although the dwell-time effect is still present, the sample is located inside the flattest region.

Since dwell time effects apply to all types of rastered polishing systems, edge protection is universally applicable to them. If we find it necessary to use epoxy, the polishing rate of the epoxy glue is adjusted to more closely match that of the die.

We accomplish this by adding a filler to the epoxy -- either alumina powder or fine glass beads (i.e., fine sandblasting media). If using alumina powder, be sure to use a grit that is at least as fine as the smallest polishing grit you will use.

Otherwise the epoxy will become a source of larger particles that will scratch the sample. The concentration of alumina or glass beads does not appear to be critical.

Figure 3. Computer simulation of Deterministic Polishing.

The simulation shows that, for the most part, the polishing uniformity should be very good. It only degrades near the edges and corners of the sample, where two effects come into play. The first is the expected pressure variation, which causes the ridges and peaks near (but not at) the edges and corners of the die. The second effect causes the steep drop-off in polishing rate at the edges of the sample. This is due to the polishing tool’s greatly reduced dwell time (i.e., t(x,y) in Equation 1) at the edges. At the extremes of the tool position, the tool approaches its maxim (or minimum) and then immediately reverses its motion, so the time spent at the edge of the die is much lower, compared to the center. Attempts to address this by increasing the tool dwell time near the edges of the sample were not successful, leading to exaggerated polishing rates in other locations. It should be noted that this problem would apply to all rastered polishing tools, regardless of the XY table design.

The extent of the nonlinear zone at the edges of the sample can be reduced by reducing the diameter of the polishing tool.

Mechanically-rastered systems have a lower size limit that is established by the ratio of the X and Y axes’ motion. If the ratio is N, the absolute minimum tool diameter is: Min-tool-Diameter = Ss/N.

Figure 4. Simulated polishing rate for sample with edge protection. The optimum sample processing "window" is in the central region.

(Eq’n 2)

Where Ss is the sample dimension along the slow axis of the polishing tools’ XY table.

Compensating for Sample Tilt

If the tool is any smaller, it will not hit portions of the sample.

This limitation does not apply to DP because the XY stage’s step size can be set to whatever is necessary to ensure good tool coverage. However, since rastered polishing systems only polish a small location at any one time, the time needed to process a sample will go up as the tool diameter decreases.

Simulations have shown that DP should be able to compensate for tilted samples, by varying the dwell time in a linear fashion across the sample. See Figure 5. Figures 6 and 7 illustrate a real-world example of tilt compensation using DP.

We also simulated an “edge protection” scheme, where the die is surrounded by an encapsulation material -- either by the molding compound that is around the die in its package, or by 132

Figure 5. Simulation showing polishing variation across a sample. The tool dwell time increased as it moved to the right.

This simulation assumes that edge protection is also used.

Figure 7. Sample at end of DP procedure.

Implementation The prototype DP hardware is shown in Figure 8. Each axis of the XY stage is driven by an RC servo motor and a rack and pinion gear. Many parts were purchased at hobbyist robotics supply stores. The programmable stage is mounted on a commercially-available milling/polishing machine. A small microcontroller is used to convert commands from the PC to the PWM-modulated signals used to control the R/C servos.

Not shown: a PC running the user interface.

Figure 6. Photo of sample prior to DP procedure. It would be very difficult to prevent over-polishing in the lower left region of the die using conventional polishing techniques.

Figure 8. DP hardware installed on an ASAP-1. No modifications to the tool are required to use the DP hardware.

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Discussion As implemented here, DP cannot achieve the nanometer-level precision reported by optics workers [2]. This probably is not a fundamental limitation so there is room for improvement, if needed. The “measurement” portion of the process is a simple microscopic examination of the sample, so adjustments to the dwell time are made by editing the job file based on observation and experience. Currently there is no automation of the measurement-correction sequence. However, in our experience, visual inspection and subsequent adjustments of the polishing parameters in an iterative fashion can yield very good results. Figs 6 and 7 illustrate this point.

Conclusion Modeling the polishing process has shown that there are a number of effects that affect all rastered polishing systems.

Simulation and real world results have shown that polishing uniformity is best when the dwell time over each portion of the sample is uniform, and when the edges of the sample are protected -- either by the package molding compound, or with epoxy that is placed around the die as part of the sample mounting procedure. Edge protection was found to be a useful sample preparation technique for all types of rastered polishing systems (it also works very well for more traditional parallellap techniques). Deterministic Polishing addresses a number of polishing uniformity issues and permits the use of smaller polishing bits. In addition, DP can compensate for sample tilt.

These improvements are particularly applicable to devices that would be damaged by the acids used to perform chemical decapsulation.

Acknowledgements All computer-rendered images were generated using openDX, an open source scientific visualization program running on Cygwin. The microcontroller is from the Arduino open source project.

All references to commercially offered products are © their respective owners.

References [1] Preston, F. W., “The Theory and Design of Glass Plate Polishing Machines”, J. Soc. Glass Technology, 11, 247 (1927) [2] Arkwright J. et al, “A Deterministic Optical Figure Correction Technique That Preserves Precision-Polished Surface Quality”, Optics Express Vol 16 No. 18 (2008) [3] Yutaka Uda et al, “Digital Polishing Method for CMP System Using a Smaller Diameter Polishing Pad” Nikon webpublished article.

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