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Open Access Publications from the University of California

Co-optimization of Restrictive Patterning Technologies and Design

  • Author(s): Badr, Yasmine
  • Advisor(s): Gupta, Puneet
  • et al.
Abstract

As the semiconductor industry strives to find novel technology scaling methods, the advanced technologies (sub-15nm) have become restrictive to the design. The restrictions implied by each technology affect the design metrics like area, delay, and power. Thus, the semiconductor industry has resorted to Design and Technology Co-Optimization (DTCO) in order to develop technologies whose characteristics are desirable from the perspective of design as well as the fabrication. This gave rise to the need for fast design-informed technology evaluation methods.

In the first part of this dissertation, we propose frameworks to address challenges of developing a new technology. The evaluation of technology impact on design is traditionally inferred from the evaluation of Design Rules (DRs). The traditional approach of evaluating DRs on the standard cell level is misleading for two reasons. First, a lot of designs are routing-limited and, hence, not every change in cell area results in a corresponding change in chip area. Second, a design rule change, which leads to a change in the delay, can affect chip area due to the buffering and gate sizing techniques required to meet timing requirements. Thus, we present Chip-DRE, a framework for Chip-scale systematic Evaluation of DRs and their interaction with layouts, performance, margins and yield.

Due to sub-wavelength lithography, layouts can have low printability and, accordingly, low yield due to the existence of bad patterns even though they pass design rule checks. For that purpose we propose Pattern-DRE, which is a framework for Pattern-driven DR Evaluation. This framework can be used by the foundries to guide them on the relative importance of patterns to the routability of standard cells and to evaluate candidate new technologies from the routability aspect.

One of the very attractive candidate new technologies is Directed Self-Assembly (DSA), because it depends on the natural multiplicative capabilities of block copolymers, which can increase the resolution and at the same time it is a relatively low-cost technology. However, DSA imposes unique constraints on the design. Therefore, in the second part of this dissertation, we focus on algorithms to enable Directed Self-Assembly (DSA).

DSA requires the use of another lithography technique in order to print templates that guide the self-assembly process. Therefore, optimizing a DSA-based process requires the choice of another patterning technique as well as optimizing the properties of the block copolymer used. For that purpose we propose DSA-Pathfind, a tool for technology pathfinding for DSA. DSA-Pathfind can be used to make choices including the number of exposures needed in printing the templates, the natural pitch of the BCP and the relevant design rules, for the objective of design-friendliness.

In order to enable the adoption of DSA in the industry, fast and chip-scalable heuristics are needed for DSA-grouping and Mask assignment for the hybrid DSA-MP process. We present an efficient heuristic algorithm for that purpose. Results show that the proposed heuristics are 39x-192x faster on the average, and result in 12%-32% more violations, in comparison to the optimal problem solution. Then, we propose a heuristic for hotspot-aware DSA grouping and MP decomposition.

Finally, we look at potential non-traditional technology scaling boosters. We propose the use of a buried layer interconnect as a scaling booster. Results show that it can save chip area by 9-13%, with negligible hit on performance. We also evaluated the possibility of relieving routing congestion using supervia, a double-height via between two non-adjacent metal layers without a landing pad on the intermediate layer.

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