Frank E. Gennari

DRCPlus in a router: automatic elimination of lithography hotspots using 2D pattern detection and correction

As technology processes continue to shrink, standard design rule checking (DRC) has become insufficient to guarantee design manufacturability. DRCPlus is a powerful technique for capturing yield detractors related to complex 2D situations1,2. DRCPlus is a pattern-based 2D design rule check beyond traditional width and space DRC that can identify problematic 2D configurations which are difficult to manufacture. This paper describes a new approach for applying DRCPlus in a router, enabling an automated approach to detecting and fixing known lithography hotspots using an integrated fast 2D pattern matching engine. A simple pass/no-pass criterion associated with each pattern offers designers guidance on how to fix these problematic patterns. Since it does not rely on compute intensive simulations, DRCPlus can be applied on fairly large design blocks and enforced in conjunction with standard DRC in the early stages of the design flow. By embedding this capability into the router, 2D yield detractors can be identified and fixed by designers in a push-button manner without losing design connectivity. More robust designs can be achieved and the impact on parasitics can be easily assessed. This paper will describe a flow using a fast 2D pattern matching engine integrated into the router in order to enforce DRCPlus rules. An integrated approach allows for rapid identification of hotspot patterns and, more importantly, allows for rapid fixing and verification of these hotspots by a tool that understands design intent and constraints. The overall flow is illustrated in Figure 1. An inexact search pattern is passed to the integrated pattern matcher. The match locations are filtered by the router through application of a DRC constraint (typically a recommended rule). Matches that fail this constraint are automatically fixed by the router, with the modified regions incrementally re-checked to ensure no additional DRCPlus violations are introduced.

Systematic data mining using a pattern database to accelerate yield ramp

Pattern-based approaches to physical verification, such as DRC Plus, which use a library of patterns to identify problematic 2D configurations, have been proven to be effective in capturing the concept of manufacturability where traditional DRC fails. As the industry moves to advanced technology nodes, the manufacturing process window tightens and the number of patterns continues to rapidly increase. This increase in patterns brings about challenges in identifying, organizing, and carrying forward the learning of each pattern from test chip designs to first product and then to multiple product variants. This learning includes results from printability simulation, defect scans and physical failure analysis, which are important for accelerating yield ramp. Using pattern classification technology and a relational database, GLOBALFOUNDRIES has constructed a pattern database (PDB) of more than one million potential yield detractor patterns. In PDB, 2D geometries are clustered based on similarity criteria, such as radius and edge tolerance. Each cluster is assigned a representative pattern and a unique identifier (ID). This ID is then used as a persistent reference for linking together information such as the failure mechanism of the patterns, the process condition where the pattern is likely to fail and the number of occurrences of the pattern in a design. Patterns and their associated information are used to populate DRC Plus pattern matching libraries for design-for-manufacturing (DFM) insertion into the design flow for auto-fixing and physical verification. Patterns are used in a production-ready yield learning methodology to identify and score critical hotspot patterns. Patterns are also used to select sites for process monitoring in the fab. In this paper, we describe the design of PDB, the methodology for identifying and analyzing patterns across multiple design and technology cycles, and the use of PDB to accelerate manufacturing process learning. One such analysis tracks the life cycle of a pattern from the first time it appears as a potential yield detractor until it is either fixed in the manufacturing process or stops appearing in design due to DFM techniques such as DRC Plus. Another such analysis systematically aggregates the results of a pattern to highlight potential yield detractors for further manufacturing process improvement.

Applying machine learning to pattern analysis for automated in-design layout optimization

Building on previous work for cataloging unique topological patterns in an integrated circuit physical design, a new process is defined in which a risk scoring methodology is used to rank patterns based on manufacturing risk. Patterns with high risk are then mapped to functionally equivalent patterns with lower risk. The higher risk patterns are then replaced in the design with their lower risk equivalents. The pattern selection and replacement is fully automated and suitable for use for full-chip designs. Results from 14nm product designs show that the approach can identify and replace risk patterns with quantifiable positive impact on the risk score distribution after replacement.

Methodology for analyzing and quantifying design style changes and complexity using topological patterns

In order to maximize yield, IC design companies spend a lot of effort to analyze what types of design styles are needed and used in their layouts (standard cells, macros, routing layers, and so forth). This paper introduces a novel methodology for full chip high performance topological pattern analysis and the applications of this methodology towards analyzing design styles in order to quantify and measure design changes and the degree of layout regularization. This new approach allows engineers to perform a full profiling across all patterns that exist in design and without needing to explicitly specify what patterns to analyze.

Methodology to extract, data mine and score geometric constructs from physical design layouts for analysis and applications in semiconductor manufacturing

At advanced technology nodes (sub-22 nm), design rules become very complicated as interactions between multiple layers become more complex, while the number of design elements within the optical radius increases. As a result, one may possibly encounter novel yield limiters in the 2D/3D design space with every new product taping out to the fab. Key to fast yield ramp is identifying novel constructs that may become yield detractors, and to address the challenge in the DFM space before actual Silicon is run. A comprehensive methodology to find such geometric constructs is proposed.

A pattern-driven design regularization methodology

Pattern matching tools have become increasingly common in physical design flows for verification and layout analysis. Recently developed topological-based pattern matching engines offer several advantages over conventional three-value logic implementations. In this paper the use of such topological engines is explored for measuring physical design regularity, driving improvements in overall regularity, and for implementing targeted enhancements for suboptimal layout configurations.

Pattern-based analytics to estimate and track yield risk of designs down to 7nm

Topological pattern-based methods for analyzing IC physical design complexity and scoring resulting patterns to identify risky patterns have emerged as powerful tools for identifying important trends and comparing different designs. In this paper, previous work is extended to include analysis of layouts designed for the 7nm technology generation. A comparison of pattern complexity trends with respect to previous generations is made. In addition to identifying topological patterns that are unique to a particular design, novel techniques are proposed for scoring those patterns based on potential yield risk factors to find patterns that pose the highest risk.

Systematic physical verification with topological patterns

Design rule checks (DRC) are the industry workhorse for constraining design to ensure both physical and electrical manufacturability. Where DRCs fail to fully capture the concept of manufacturability, pattern-based approaches, such as DRC Plus, fill the gap using a library of patterns to capture and identify problematic 2D configurations. Today, both a DRC deck and a pattern matching deck may be found in advanced node process development kits. Major electronic design automation (EDA) vendors offer both DRC and pattern matching solutions for physical verification; in fact, both are frequently integrated into the same physical verification tool. In physical verification, DRCs represent dimensional constraints relating directly to process limitations. On the other hand, patterns represent the 2D placement of surrounding geometries that can introduce systematic process effects. It is possible to combine both DRCs and patterns in a single topological pattern representation. A topological pattern has two separate components: a bitmap representing the placement and alignment of polygon edges, and a vector of dimensional constraints. The topological pattern is unique and unambiguous; there is no code to write, and no two different ways to represent the same physical structure. Furthermore, markers aligned to the pattern can be generated to designate specific layout optimizations for improving manufacturability. In this paper, we describe how to do systematic physical verification with just topological patterns. Common mappings between traditional design rules and topological pattern rules are presented. We describe techniques that can be used during the development of a topological rule deck such as: taking constraints defined on one rule, and systematically projecting it onto other related rules; systematically separating a single rule into two or more rules, when the single rule is not sufficient to capture manufacturability constraints; creating test layout which represents the corners of what is allowed, or not allowed by a rule; improving manufacturability by systematically changing certain patterns; and quantifying how a design uses design rules. Performance of topological pattern search is demonstrated to be production full-chip capable.