Layout schema generation: Improving yield ramp during technology development

Layout schema generation generates random, realistic, DRC-clean layout patterns of the new design technology for use in test vehicles.

BY WAEL ELMANHAWY and JOE KWAN, Mentor Graphics, Beaverton, OR

Predicting and improving yield in the early stages of technology development is one of the main reasons we create test macros on test masks. Identifying potential manufacturing failures during the early technology development phase lets design teams implement upstream corrective actions and/or process changes that reduce the time it takes to achieve the desired manufacturing yield in production. However, while conventional yield ramp techniques for a new technology node rely on using designs from previous technology nodes as a starting point to identify patterns for design of experiment (DoE) creation, what do you do in the case of a new design technology, such as multi- patterning, that did not exist in previous nodes? The human designer’s experience isn’t applicable, since there isn’t any knowledge about similar issues from previous designs. Neither is there any prior test data from which designers can draw feedback to create new test structures, or identify process or design style optimizations that can improve yield more quickly.

An innovative new technology, layout schema gener- ation (LSG), enables design teams to generate additional macros to add to test structures without relying on past designs for input. These macros are based on the gener- ation and random placement of unit patterns that can construct more meaningful larger patterns. Specifications governing the relationships between those unit patterns can be adjusted to generate layout clips that look like realistic designs. Those layout clips can then be used in design of experiment (DoE) trials to predict yield, and identify potential design and process optimizations that will help improve yield. By using this new LSG process, designers can significantly reduce the time it takes to achieve the desired yield for designs that include new design techniques.

Issues affecting yield

Wafer yield is typically reduced by three categories of defects. The first category comprises random defects, which occur due to the existence of contamination particles in the different process chambers. A conducting particle can short out two or more neighboring wires, or create a leakage path. A non-conducting particle or a void can open up a wire or a via, or create high resistive paths. FIGURE 1 shows scanning electron microscope (SEM) images of these two types of random defects.

The second category contains systematic defects, which occur due to an imperfect physical layout architecture, or the impact of non-optimized optical process recipes and/ or equipment. Systematic defects are typically the biggest source of yield detraction [1], but a majority of them can be eliminated through design-technology co-optimization (DTCO), in which the design and process sides commu- nicate more freely to achieve faster rates of improvement.

The third category, which we’re not addressing in this article, includes parametric defects (such as a lack of uniformity in the doping process) that may affect the reliability of devices.

Layout schema generation

To demonstrate the use and applicability of the LSG process, let’s look at designs that use the self-aligned multi-patterning (SAMP) process. Multi-patterning (MP) technology with ArF 193i lithography is currently the preferred choice over extreme ultraviolet (EUV) lithography for advanced technology nodes from 20nm on down. At 7 nm and 5 nm nodes, the SAMP process appears to be one of the most effective MP techniques in terms of achieving a small pitch of printed lines on the wafer, but its yield is in question. Of course, before being deployed in production, it must be thoroughly tested on test vehicles. However, without any previous SAMP designs, design of an appropriate test vehicle is challenging. In addition to the lack of historical test data, the unidirectional nature of the SAMP design complicates the design of the conven- tional serpentine and comb test shapes, which contain bidirectional components.

Self-aligned multi-patterning process

In the SAMP process [3], the first mask is known as the mandrel mask. Sacrificial mandrel shapes are printed with a relaxed pitch, and then used to develop sidewalls. The sidewalls are at half the mandrel’s pitch. Depending on the tone, target shapes may exist in the spaces between the sidewalls. The target shapes can be reused as sacrificial mandrel shapes to form another generation of sidewalls. Wafer shapes that don’t have corresponding mask shapes are called non-mandrel shapes. This process can be repeated to achieve SAMP layouts with a reduced pitch. The SAMP process (FIGURE 2) restricts the designs to be almost unidirectional. Generated parallel lines will be cut later by a cut mask at the desired line ends to form the correct connectivity.

Test vehicles

A test vehicle is typically a subset of the masks for a design, designed specifically to induce potential systematic failures or lithographic hotspots on the layer under test. It may also contain some test structures specially designed for the detection of random defects. The main compo- nents in a test vehicle for any new node are serpentine and comb shapes (to capture random defects), and preliminary standard cell designs (with many variations, to assess their quality). Other structures are typically added based on experience derived from production chips of previous nodes.

In a new node, all test structures on the test vehicle are vital for process training and characterization. Feedback from the test process is used for design style optimization. For example, when “bad” layout geometries are discovered after manufacturing, they can be captured as patterns, assigned low scores, and stored in a design for manufacturing (DFM) pattern library [2]. The designer can then use DFM analysis to find the worst patterns in a given layout, and modify or eliminate them. Such early DTCO provides a faster yield ramp for new nodes. Even in mature nodes, test structures are used on production wafers to identify additional opportunities for process refinement and optimization, which will have a positive impact on future yield.

One of the obstacles in test vehicle design is that it depends mainly on human designer’s experience and memory. Although experienced designers have seen multiple design styles in older nodes, the design shapes they are familiar with are limited to those styles. It typically takes a long time to design new test structures that cover new shapes, especially for a new process. The LSG solution adds more macros (generated in a random fashion) to the standard test structures strategy to speed up new shape yield analysis.

Random test pattern generation

The key component of the LSG solution is a method for the random generation of realistic design-like layouts, without design rule violations. The LSG process uses a Monte Carlo method to apply randomness in the generation of layout clips by inserting basic unit patterns in a grid. These unit patterns represent simple rectangular and square polygons, as well as a unit pattern for inserting spaces in the design. Unit pattern sizes depend on the technology pitch value. During the generation of the layouts, known design rules are applied as constraints for unit pattern insertion. Once the rules are configured, an arbitrary size of layout clips can be generated (FIGURE 3).

To begin, the SAMP design rules are converted to a format readable by an automated LSG tool like the Calibre® LSG tool from Mentor, a Siemens Business. Once the rules are configured, the Calibre LSG process can automatically generate an arbitrarily wide area of realistic DRC-clean SAMP patterns. The area is only limited by the floorplan of the designated macro of SAMP test structures. Test patterns can be also generated with power rails to mimic the layouts of standard cells. FIGURE 4 shows a sample clip of the generated output layout. To be ready for the experiments, the SAMP design is decomposed into the appropriate mandrel and cut masks, according to the decomposition rules. This operation also distinguishes between mandrel and non-mandrel shapes.

Design of Experiment

In the design phase of the test vehicle, the generated SAMP patterns are added to the typical contents of regular test patterns. The random SAMP patterns are electrically meaningless, unless they are connected to other layers to set up the required experiment. The DoE determines the way the connections are made from the patterns up to the testing pads, to detect different fail modes. Fail modes include short circuits due to lithographic bridging or conducting particles, and open circuits due to lithographic pinching, non-conducting particles, voids, or open vias.

A via chain can be constructed to connect the random DoE of SAMP structures through a routing layer to external pads for electrical measurement. These clips are decomposed according to the decomposition rules of the technology into the appropriate mandrel and cut masks. The decomposed clips can be tested through simulations, or electrically on silicon to discover hotspots. The discovered hotspots can be analyzed to determine root cause, which can be used to modify design layouts and/or optimize the fabrication process and models to eliminate these hotspots in future production. They can also be used as learning patterns for DFM rule deck devel- opment. By expanding the size of the randomly generated test structures, more hotspots can be detected, which can provide an even faster way to enhance the yield of a new technology node.

To demonstrate the effectiveness of the LSG process, we performed two experiments on a set of SAMP patterns similar to those shown in FIGURE 4.

Detecting random conducting particles

The first experiment collected data about random defects caused by conducting particles. In this experiment, all mandrel shapes are connected through the upper (or lower) via and metal layers, up to a testing pad. All non-mandrel shapes are connected in the same way to another testing pad. The upper routing layer forms two interdigitated comb shapes. FIGURE 5 shows a layout snippet of the connections. All via placements and upper metal routings were made with a custom script, without the intervention of a human designer. Ideally the two testing pads should be disconnected, as no mandrel shape can touch a non-mandrel shape. If the testing probes are found to be connected, this likely indicates a random conducting particle defect, or a lithographic bridge. The localization and analysis of such defects [4] can help with yield estimation and enhancement.

Detecting systematic cut mask resolution problems

One example of a systematic lithographic defect found in SAMP designs is when the cut mask is not resolved correctly. This causes two shapes on the same track to be shorted out through the unresolved cut shape. The testing of such a case requires connecting every other polygon on the same track. This was done with a generating script, without the intervention of human designers. FIGURE 6 shows a snippet of the generated layout with the connections. If the test probes are found to be connected while the two pads (ideally) are disconnected, this may indicate an unresolved cut shape. The analysis of the defect location and data from multiple wafers can prove the root cause of the defect.

Results

The two experiments described above were placed on a test vehicle of an advanced node. The test macro containing the first experiment setup successfully detected several conducting particle defects. A sample SEM image of the discovered defect is shown in FIGURE 7. Statistical data from multiple wafers were used to model the defect density and estimate the yield target.

Repetitive fail data from the test macro of the second experiment indicated systematic failures at particular locations. The analysis showed that the root cause of the failure was a poorly resolving cut shape in some process corners, as was predicted in the DoE. FIGURE 8 shows a snippet of the generated layout and its contour simulation.

To test the effectiveness of the random approach in capturing defects, 20 SAMP design clips were generated with linearly increasing sizes, such that the 20th clip was 20X bigger than the first clip. Lithography simulations were executed on the cut mask to inspect potential failures. The contours were checked, and potential failures were identified and categorized. FIGURE 9 shows the number of the unique hotspots found in each clip. The graph shows that the number of identified hotspots tends to saturate with the chip size. The second clip has 2X the number of unique hotspots found in the first clip, while the 20th clip only sees around a 6X increase. This result is expected, as many hotspots in the larger clips are just replicas of those found in the small clips. Assuming that the LSG tool is configured correctly, this result means most of the potential hotspots can be covered in a reasonable size test vehicle.

Conclusion

Test vehicles are vital for yield ramp up in new technologies and yield enhancement in mature nodes, but it can be difficult to design accurate test structures for new design styles and technologies that have no relevant history. Innovative techniques are needed to achieve comprehensive coverage of potential manufacturing failures created by new design styles, while ensuring full compliance with known design rule checks. A new solution using layout schema generation generates random, realistic, DRC-clean layout patterns of the new design technology for use in test vehicles. Experiments with this technology show it can provide high coverage of new design styles for an arbitrarily-wide design area. Circuitry can be added to the generated clips to make them electrically measurable for the detection of potential failures. The ability to discover lithographic hotspots and systematic failures early in the technology development process is significantly improved, at the expense of additional testing area. This design/technology co-optimization speeds up the yield optimization for new technology nodes, improving a critical success factor for market success.

References

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