Crosstalk minimization of local channel routing algorithms in VLSI CAD

The greedy and left edge algorithms, as applied to local routing in VLSI CAD, were modified to decrease crosstalk between neighboring wires. The modifications on the algorithms use spacing, and segregation to improve the routing of wires in a channel. The modified greedy and left edge algorithms use a grid, but the minimum distance between two wires can be varied depending on the crosstalk between them. Crosstalk information must be obtained separately and is part of the required set of inputs to the algorithms. The improved algorithms route all the channel problems tested in less tracks than the original algorithms, if crosstalk constraints exist, and in the same number of tracks, if no crosstalk constraints exist

Layout optimization in ultra deep submicron VLSI design

As fabrication technology keeps advancing, many deep submicron (DSM) effects have become increasingly evident and can no longer be ignored in Very Large Scale Integration (VLSI) design. In this dissertation, we study several deep submicron problems (eg. coupling capacitance, antenna effect and delay variation) and propose optimization techniques to mitigate these DSM effects in the place-and-route stage of VLSI physical design. The place-and-route stage of physical design can be further divided into several steps: (1) Placement, (2) Global routing, (3) Layer assignment, (4) Track assignment, and (5) Detailed routing. Among them, layer/track assignment assigns major trunks of wire segments to specific layers/tracks in order to guide the underlying detailed router. In this dissertation, we have proposed techniques to handle coupling capacitance at the layer/track assignment stage, antenna effect at the layer assignment, and delay variation at the ECO (Engineering Change Order) placement stage, respectively. More specifically, at layer assignment, we have proposed an improved probabilistic model to quickly estimate the amount of coupling capacitance for timing optimization. Antenna effects are also handled at layer assignment through a linear-time tree partitioning algorithm. At the track assignment stage, timing is further optimized using a graph based technique. In addition, we have proposed a novel gate splitting methodology to reduce delay variation in the ECO placement considering spatial correlations. Experimental results on benchmark circuits showed the effectiveness of our approaches

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

Track Assignment Considering Crosstalk-Induced Performance Degradation

Track assignment is a critical step between global routing and detailed routing in modern VLSI chip designs. It greatly affects some very important design characteristics, such as routability, via usage and timing performance. Crosstalk, which is largely decided by wire adjacency, has significant impact on interconnect delay and circuit performance. Therefore, the amount of crosstalk should be restrained in order to satisfy timing constraints. In this work, a track assignment approach is proposed to control crosstalk-induced performance degradation. The problem is formulated as a Traveling Salesman Problem (TSP) and solved by a graph-based heuristic. The proposed approach is implemented and tested on benchmark circuits from the ISPD2011 contest and the experimental results are quite promising

물리적 설계 자동화에서 표준셀 합성 및 최적화와 설계 품질 예측 방법론

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2023. 2. 김태환.In the physical design of chip implementation, designing high-quality standard cell layout and accurately predicting post-route DRV (design rule violation) at an early stage is an important problem, especially in advanced technology nodes. This dissertation presents two methodologies that can contribute to improving the design quality and design turnaround time of physical design flow. Firstly, we propose an integrated approach to the two problems of transistor folding and placement in standard cell layout synthesis. Precisely, we propose a globally optimal algorithm of search tree based design space exploration, devising a set of effective speeding up techniques as well as dynamic programming based fast cost computation. In addition, our algorithm incorporates the minimum oxide diffusion jog constraint, which closely relies on both of transistor folding and placement. Through experiments with the transistor netlists and design rules in advanced node, our proposed method is able to synthesize fully routable cell layouts of minimal size within a very fast time for each netlist, outperforming the cell layout quality in the manual design. Secondly, we propose a novel ML based DRC hotspot prediction technique, which is able to accurately capture the combined impact of pin accessibility and routing congestion on DRC hotspots. Precisely, we devise a graph, called pin proximity graph, that effectively models the spatial information on cell I/O pins and the information on pin-to-pin disturbance relation. Then, we propose a new ML model, which tightly combines GNN (graph neural network) and U-net in a way that GNN is used to embed pin accessibility information abstracted from our pin proximity graph while U-net is used to extract routing congestion information from grid-based features. Through experiments with a set of benchmark designs using advanced node, our model outperforms the existing ML models on all benchmark designs within the fast inference time in comparison with that of the state-of-the-art techniques.칩 구현의 물리적 설계 단계에서, 높은 성능의 표준 셀 설계와 배선 연결 이후 조기에 설계 규칙 위반을 정확히 예측하는 것은 최신 공정에서 특히 중요한 문제이다. 본 논문에서는 물리적 설계에서의 설계 품질과 총 설계 시간 향상을 달성할 수 있는 두 가지 방법론을 제안한다. 먼저, 본 논문에서는 표준 셀 레이아웃 합성에서 트랜지스터 폴딩과 배치를 종합적으로 진행할 수 있는 방법론을 논한다. 구체적으로 탐색 트리 기반의 최적화 알고리즘과 동적 프로그래밍 기반 빠른 비용 계산 방법과 여러 속도 개선 기법을 제안한다. 여기에 더해, 최신 공정에서 트랜지스터 폴딩과 배치로 인해 발생할 수 있는 최소 산화물 확산 영역 설계 규칙을 고려하였다. 최신 공정에 대한 표준 셀 합성 실험 결과, 본 논문에서 제안한 방법이 설계 전문가가 수동으로 설계한 것 대비 높은 성능을 보이고, 설계 시간도 매우 짧음을 보인다. 두번째로, 본 논문에서는 셀 배치 단계에서 핀 접근성과 연결 혼잡으로 인한 영향을 종합적으로 고려할 수 있는 머신 러닝 기반 설계 규칙 위반 구역 예측 방법론을 제안한다. 먼저 표준 셀의 입/출력 핀의 물리적 정보와 핀과 핀 사이 방해 관계를 효과적으로 표현할 수 있는 핀 근접 그래프를 제안하고, 그래프 신경망과 유넷 신경망을 효과적으로 결합한 새로운 형태의 머신 러닝 모델을 제안한다. 이 모델에서 그래프 신경망은 핀 근접 그래프로부터 핀 접근성 정보를 추출하고, 유넷 신경망은 격자 기반 특징으로부터 연결 혼잡 정보를 추출한다. 실험 결과 본 논문에서 제안한 방법은 이전 연구들 대비 더 빠른 예측 시간에 더 높은 예측 성능을 달성함을 보인다.1 Introduction 1 1.1 Standard Cell Layout Synthesis 1 1.2 Machine Learning for Electronic Design Automation 6 1.3 Prediction of Design Rule Violation 8 1.4 Contributions of This Dissertation 11 2 Standard Cell Layout Synthesis of Advanced Nodes with Simultaneous Transistor Folding and Placement 14 2.1 Motivations 14 2.2 Algorithm for Standard Cell Layout Synthesis 16 2.2.1 Problem Definition 16 2.2.2 Overall Flow 18 2.2.3 Step 1: Generation of Folding Shapes 18 2.2.4 Step 2: Search-tree Based Design Space Exploration 20 2.2.5 Speeding up Techniques 23 2.2.6 In-cell Routability Estimation 28 2.2.7 Step 3: In-cell Routing 30 2.2.8 Step 4: Splitting Folding Shapes 35 2.2.9 Step 5: Relaxing Minimum-area Constraints 37 2.3 Experimental Results 38 2.3.1 Comparison with ASAP 7nm Cell Layouts 40 2.3.2 Effectiveness of Dynamic Folding 42 2.3.3 Effectiveness of Speeding Up Techniques 43 2.3.4 Impact of Splitting Folding Shape 48 2.3.5 Runtime Analysis According to Area Relaxation 51 2.3.6 Comparison with Previous Works 52 3 Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction using Graph Neural Network and U-Net 54 3.1 Preliminary 54 3.1.1 Graph Neural Network 54 3.1.2 Fully Convolutional Network 56 3.2 Proposed Prediction Methodology 57 3.2.1 Overall Flow 57 3.2.2 Pin Proximity Graph 58 3.2.3 Grid-based Features 61 3.2.4 Overall Architecture of PGNN 64 3.2.5 GNN Architecture in PGNN 64 3.2.6 U-net Architecture in PGNN 66 3.2.7 Final Prediction in PGNN 66 3.3 Experimental Results 68 3.3.1 Experimental Setup 68 3.3.2 Analysis on PGNN Performance 71 3.3.3 Comparison with Previous Works 72 3.3.4 Adaptation to Real-world Designs 81 3.3.5 Handling Data Imbalance Problem in Regression Model 86 4 Conclusions 92 4.1 Chapter 2 92 4.2 Chapter 3 93박

COBE's search for structure in the Big Bang

The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Space Communications: Theory and Applications. Volume 3: Information Processing and Advanced Techniques. A Bibliography, 1958 - 1963

Annotated bibliography on information processing and advanced communication techniques - theory and applications of space communication

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity