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Scoping the Problem of DFM in the Semiconductor Industry
(6/21/2004) Future Fab Intl. Issue 17
By Andrew B. Kahng, University of California, San Diego
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The International Technology Roadmap for Semiconductors defines requirements that drive semiconductor supplier industries (e.g., electronic design automation (EDA), lithography, and packaging) toward costeffective scaling of CMOS process technology. In ITRS parlance, a “red brick” is a technology requirement for which no known solution exists. Solving any red brick requires large R&D investments. It is neither technologically feasible nor economically viable for any given supplier industry to solve red bricks in an independent attempt to continue Moore's Law scaling. This leads

For an example of a potential shared red brick, consider the multibillion-dollar question of whether lithography and frontend processes must continue to push for 10 percent tolerances in critical dimensions. As an alternative, some of the CD control red brick burden may be borne by designer and EDA constituencies, which can provide smarter design for variability techniques to enable robust, high-yielding designs without impossibly tight control of CD. Another example can be found in the design-to-mask handoff and the cost of developing faster, more accurate mask writers. Should mask equipment suppliers develop increasingly expensive and complex mask writers to handle the ever-increasing post-reticle enhancement technology (RET) fractured design data volume? As an alternative, a bidirectional design-to-mask data flow, founded on new cross-disciplinary EDA technology, can dramatically reduce data volume. Such examples as these point out a need to carefully partition R&D investment so as to maximize overall industry ROI.

Welcome to Future Fab Vol. 15

The International Technology Roadmap for Semiconductors defines requirements that drive semiconductor supplier industries (e.g., electronic design automation (EDA), lithography, and packaging) toward costeffective scaling of CMOS process technology. In ITRS parlance, a “red brick” is a technology requirement for which no known solution exists. Solving any red brick requires large R&D investments. It is neither technologically feasible nor economically viable for any given supplier industry to solve red bricks in an independent attempt to continue Moore's Law scaling. This leads to the concept of “shared red bricks” – supplier industries must cooperate to achieve a more globally optimized solution to these technological hurdles.

For an example of a potential shared red brick, consider the multibillion-dollar question of whether lithography and frontend processes must continue to push for 10 percent tolerances in critical dimensions. As an alternative, some of the CD control red brick burden may be borne by designer and EDA constituencies, which can provide smarter design for variability techniques to enable robust, high-yielding designs without impossibly tight control of CD. Another example can be found in the design-to-mask handoff and the cost of developing faster, more accurate mask writers. Should mask equipment suppliers develop increasingly expensive and complex mask writers to handle the ever-increasing post-reticle enhancement technology (RET) fractured design data volume? As an alternative, a bidirectional design-to-mask data flow, founded on new cross-disciplinary EDA technology, can dramatically reduce data volume. Such examples as these point out a need to carefully partition R&D investment so as to maximize overall industry ROI.

Invoking ROI brings up the issue of cost, both as a normalizer across supplier industries and as a critical indicator of future semiconductor industry health. The semiconductor industry must extract incremental value from incremental manufacturing capability. When designers cannot predictably fill the fab with highvalue, high-margin parts, there is less return on a foundry’s capital investments, leading to workarounds (reprogrammability, platform-based design, software-based product differentiation, and so on) that provide value through means other than silicon differentiation. Such workarounds sacrifice quality and value in designs, meaning fewer design projects are worth attempting. Thus, design and process technologists are in the same boat, trying to maintain the retooling cycles that are the heartbeat of the semiconductor roadmap. Cost is indeed the semiconductor industry’s greatest challenge.

Today, design NRE costs routinely reach tens of millions of dollars, in part due to the lack of manufacturing closure in today’s design flows, which causes longer turnaround times and manufacturing respins. Future design for manufacturing (DFM) technology must reduce design NRE cost and directly address manufacturing NRE – the cost of a mask set and probe card – which is well over $1 million at the 90 nm technology node and creates a significant damper on semiconductor-based innovation. As the semiconductor industry deploys optical proximity corrections, phase-shifting masks, and area fill to better control subwavelength lithography, it would be a mistake to simply concede the multimilliondollar mask set as a fact of life. The challenges for DFM are to reduce these NRE costs and to maximize ROI and productivity.

As the industry works to solve the challenges of DFM and manufacturing yield, it will have to address structural barriers and the creation of “vertical” designers and tools. The current era of independent silos or communities – design, process, IP libraries, EDA, etc. – must come to an end. People as well as tools must gain increased understanding of the entire design-to-die flow, not just small isolated segments. Today’s attempts at DFM strongly reflect technology DNA or genealogy – physical verification, custom layout, optical lithography simulation, reticle enhancement, place-androute, performance analysis, etc. Traditional separations such as FEOL vs. BEOL, design synthesis and optimization vs. design analysis and verification, and design engineer vs. process engineer unfortunately persist in the DFM arena. At the same time, closer interaction between traditionally independent analysis and optimization technologies will improve the chances of reaching faster and more economical solutions. In particular, the EDA industry must fill gaps that today prevent close alignment of design and manufacturing flows. EDA tools targeted primarily at post-tapeout enhancements such as RET and CMP-fill need to move up the food chain to interact more closely with synthesis, timing optimization, and place and route flows. Particularly as process variability increases in future technology nodes, DFM will be responsible for avoiding excessive over-design as well as lithography costs.

Future Vertical Tool Integrations

First, a unified and standardized data model can be enabling to design-throughmanufacturing integrations. One candidate, the universal data model (UDM), is under development (see www.si2.org/udm/); its goal is to allow both the design and manufacturing sides to access a fuller set of design information and manufacturing characterizations. Such a model must be sufficiently restrictive so that foundries, mask shops, and design houses can allow data to cross boundaries; yet it must be extensible enough to permit closer sharing of data as available. The present conception of UDM encompasses design geometry data, parasitics, connectivity information, timing constraints, and mask features, among other attributes. As a result, it potentially forms an infrastructure for lithography-aware design technology as well as design-aware lithography.

Second, the absence of design-for-yield and DFM methodologies is often blamed on the absence of usable (statistical) process data. Besides being highly proprietary to the foundries, the problem with such data lies in its extraction. Extraction of quantified process characteristics that can be used by upstream design tools is highly involved. It may require extensive collaboration between EDA vendors, manufacturing equipment makers, foundries, mask makers, and metrology equipment makers, among others. It may further require fabrication and analysis of several test chips. Moreover, such process characterization can differ from tool to tool, material to material, and location to location, even for the same foundry. A more practical vision is for design optimization and analysis tools to instead make use of qualitative universal truths about the manufacturing process. Such tools would essentially try to exploit knowledge of trends to avoid the need for extensive process data.

Third, post-tapeout processing tools such as RET and fill-insertion tools must be made aware of design quality metrics (i.e., chip metrics) such as timing and power. Such awareness not only aligns the tools to the overall product goals, but also contributes to parametric yield enhancements and enables much-needed reductions in manufacturing costs and mask data preparation. Due to the potential impact of such data processing steps for manufacturability, post-RET performance simulation would be desirable going into the future. This would require a more direct communication channel between mask data prep and design, which would initially be more tractable for IDMs rather than fab-less design houses. Two noteworthy illustrations of this channel can be given for optical proximity correction (OPC) and area fill.

OPC changes mask apertures to correct for line-end shortening, corner rounding, and other systematic optical process effects. The resulting complicated mask shapes affect data volumes and design rule checking, and even more critically impact mask writing and inspection costs. For example, future design technology must allow function-aware OPC that is applied only as needed (e.g., to features in critical paths that require better CD control). This entails passing performance analysis and functional intentions from logic-layout synthesis to physical verification. Required flow integrations must span library creation, detailed routing, and physical verification (e.g., so that an optical correction is not made independently by several tools, leading to an incorrect result). Such passing of designers' intent to OPC tools can lead to a functionally better OPC result in addition to huge mask cost savings.

“Dummy” area fill insertion must not only be driven by the best possible models of the manufacturing process, but also address many flow issues. Examples include: a) dummy metal fill will change RC extraction results and must be accounted for in the upstream timing verification before the layout goes to physical verification; b) master cell and macro characterizations (performance models) must be a priori compatible with later insertion of dummy fill; c) data volumes and design hierarchies must be maintained or at least strongly managed; and d) interlayer dependencies such as filling/cheesing (active/poly) or the need to maximize planarity of the sum of all layer thicknesses. Just as with OPC and PSM, dummy fill requires a completely integrated, front-to-back solution. The industry urgently needs true performanceimpact- limited fill insertion techniques, driven by competent modeling.

Finally, we close with an important word of caution about the statistical timing tools and statistical optimization methods that are actively being researched. Such tools must model and understand actual variations rather than crude and inaccurate abstractions. This means that the “composition” of variations during statistical analysis and optimizations should match the methods used for initial "decomposition" via statistical metrology techniques during process characterization. A mismatch between the two will introduce unnecessary errors and place a question mark on results produced by computeintensive statistical analyses.

 

 
 
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