Synthesis of Clock Trees with Useful Skew based on Sparse-Graph Algorithms

Computer-aided design (CAD) for very large scale integration (VLSI) involve

A Metaheuristic Method for Fast Multi-Deck Legalization

Department of Electrical EngineeringIn the field of circuit design, decreasing the transistor size is getting harder and harder. Hence, improving the circuit performance also becoming difficult. For the better circuit performance, various technologies are being tired and multi-deck standard cell technology is one of them. The standard cell methodology is a fundamental structure of EDA (Electric Design Automation). Using the standard cell library, EDA tools can easily design, and optimize the physical design of chips. In order to conventional standard cell, multi-deck standard cell occupies multiple rows on the chip. This multiple occupation increases complexity of the circuit physical design for EDA tools. Thus, legalization problem has become more challenging for the multi-deck standard cells. Recently, various multi-deck legalization methods are proposed because the conventional single-deck legalization method is not effective for multi-deck legalization. A state-of-the-arts legalization method is based on quadratic programming with the linear complementary problem(LCP). However, these previous researches can only cover the double-deck case because of runtime burden. In this thesis, we propose the fast and enhanced the multi-deck standard cell legalization algorithm which can handle higher than double-deck standard cell cases. The proposed legalization method achieves the most fastest runtime result for the dominant number of benchmarks on ICCAD Contest 2017 [1] compared with Top 3 results.ope

Macdonald topological vertices and brane condensates

We show, in a number of simple examples, that Macdonald-type $qt$-deformations of topological string partition functions are equivalent to topological string partition functions that are without $qt$-deformations but with brane condensates, and that these brane condensates lead to geometric transitions.Comment: 23 pages, 5 figures. v2: minor changes, published versio

Improved Physical Design for Manufacturing Awareness and Advanced VLSI

Increasing challenges arise with each new semiconductor technology node, especially in advanced nodes, where the industry tries to extract every ounce of benefit as it approaches the limits of physics, through manufacturing-aware design technology co-optimization and design-based equivalent scaling. The increasing complexity of design and process technologies, and ever-more complex design rules, also become hurdles for academic researchers, separating academic researchers from the most up-to-date technical issues.This thesis presents innovative methodologies and optimizations to address the above challenges. There are three directions in this thesis: (i) manufacturing-aware design technology co-optimization; (ii) advanced node design-based equivalent scaling; and (iii) an open source academic detailed routing flow.To realize manufacturing-aware design technology co-optimization, this thesis presents two works: (i) a multi-row detailed placement optimization for neighbor diffusion effect mitigation between neighboring standard cells; and (ii) a post-routing optimization to generate 2D block mask layout for dummy segment removal in self-aligned multiple patterning.To achieve advanced node design-based equivalent scaling, this thesis presents two improved physical design methodologies: (i) a post-placement flop tray generation approach for clock power reduction; and (ii) a detailed placement approach to exploit inter-row M1 routing for congestion and wirelength reduction.To address the increasing gap between academia and industry, this thesis presents two works toward an open source academic detailed routing flow: (i) a complete, robust, scalable and design ruleaware dynamic programming-based pin access analysis framework; and (ii) TritonRoute – the open source detailed router that is capable of delivering DRC-clean detailed routing solutions in advanced nodes.This thesis concludes with a summary of its contributions and open directions for future research

FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning

Design flow parameters are of utmost importance to chip design quality and require a painfully long time to evaluate their effects. In reality, flow parameter tuning is usually performed manually based on designers' experience in an ad hoc manner. In this work, we introduce a machine learning-based automatic parameter tuning methodology that aims to find the best design quality with a limited number of trials. Instead of merely plugging in machine learning engines, we develop clustering and approximate sampling techniques for improving tuning efficiency. The feature extraction in this method can reuse knowledge from prior designs. Furthermore, we leverage a state-of-the-art XGBoost model and propose a novel dynamic tree technique to overcome overfitting. Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work. Our approach is further validated on two industrial designs. By sampling less than 0.02% of possible parameter sets, it reduces area by 1.83% and 1.43% compared to the best solutions hand-tuned by experienced designers