Seongbo Shim, Yoojong Lee, and Youngsoo Shin Lithographic Defect Aware Placement Using Compact Standard Cells Without Inter-Cell Margin.

Slides:



Advertisements
Similar presentations
THERMAL-AWARE BUS-DRIVEN FLOORPLANNING PO-HSUN WU & TSUNG-YI HO Department of Computer Science and Information Engineering, National Cheng Kung University.
Advertisements

Design Rule Generation for Interconnect Matching Andrew B. Kahng and Rasit Onur Topaloglu {abk | rtopalog University of California, San Diego.
Native-Conflict-Aware Wire Perturbation for Double Patterning Technology Szu-Yu Chen, Yao-Wen Chang ICCAD 2010.
©Silberschatz, Korth and Sudarshan12.1Database System Concepts Chapter 12: Indexing and Hashing Basic Concepts Ordered Indices B+-Tree Index Files B-Tree.
Methodology for Standard Cell Compliance and Detailed Placement for Triple Patterning Lithography Bei Yu, Xiaoqing Xu, JhihRong Gao, David Z. Pan.
Adaptive Resonance Theory (ART) networks perform completely unsupervised learning. Their competitive learning algorithm is similar to the first (unsupervised)
Ahmed Awad Atsushi Takahash Satoshi Tanakay Chikaaki Kodamay ICCAD’14
CPSC 335 Computer Science University of Calgary Canada.
1 A Lithography-friendly Structured ASIC Design Approach By: Salman Goplani* Rajesh Garg # Sunil P Khatri # Mosong Cheng # * National Instruments, Austin,
Ryan Kinworthy 2/26/20031 Chapter 7- Local Search part 1 Ryan Kinworthy CSCE Advanced Constraint Processing.
Evaluation.
13. The Weak Law and the Strong Law of Large Numbers
Toward a Methodology for Manufacturability-Driven Design Rule Exploration Luigi Capodieci, Puneet Gupta, Andrew B. Kahng, Dennis Sylvester, and Jie Yang.
Detailed Placement for Leakage Reduction Using Systematic Through-Pitch Variation Andrew B. Kahng †‡ Swamy Muddu ‡ Puneet Sharma ‡ CSE † and ECE ‡ Departments,
1 Wavelet synopses with Error Guarantees Minos Garofalakis Phillip B. Gibbons Information Sciences Research Center Bell Labs, Lucent Technologies Murray.
Topography-Aware OPC for Better DOF margin and CD control Puneet Gupta*, Andrew B. Kahng*†‡, Chul-Hong Park†, Kambiz Samadi†, and Xu Xu‡ * Blaze-DFM Inc.
Copyright © 2010 Pearson Education, Inc. Chapter 19 Confidence Intervals for Proportions.
Triple Patterning Aware Detailed Placement With Constrained Pattern Assignment Haitong Tian, Yuelin Du, Hongbo Zhang, Zigang Xiao, Martin D.F. Wong.
Hsiu-Yu Lai Ting-Chi Wang A TPL-Friendly Legalizer for Standard Cell Based Design SASIMI ‘15.
SLIP 2000April 9, Wiring Layer Assignments with Consistent Stage Delays Andrew B. Kahng (UCLA) Dirk Stroobandt (Ghent University) Supported.
Lecture 6: Let’s Start Inferential Stats Probability and Samples: The Distribution of Sample Means.
UC San Diego Computer Engineering VLSI CAD Laboratory UC San Diego Computer Engineering VLSI CAD Laboratory UC San Diego Computer Engineering VLSI CAD.
Statistics for Managers Using Microsoft® Excel 7th Edition
Genetic Algorithm.
Hongbo Zhang, Yuelin Du, Martin D.F. Wong, Yunfei Deng, Pawitter Mangat Synopsys Inc., USA Dept. of ECE, Univ. of Illinois at Urbana-Champaign GlobalFoundries.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Coverage – “Systematic” Testing Chapter 20. Dividing the input space for failure search Testing requires selecting inputs to try on the program, but how.
Section 8.1 Estimating  When  is Known In this section, we develop techniques for estimating the population mean μ using sample data. We assume that.
TSV-Aware Analytical Placement for 3D IC Designs Meng-Kai Hsu, Yao-Wen Chang, and Valerity Balabanov GIEE and EE department of NTU DAC 2011.
Fundamentals of Data Analysis Lecture 10 Management of data sets and improving the precision of measurement pt. 2.
Area/Sigma Notation Objective: To define area for plane regions with curvilinear boundaries. To use Sigma Notation to find areas.
K.Yuan, J.Yang and D.Pan ECE Dept. Univ. of Texas at Austin
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology The Weak Law and the Strong.
Statistics for Managers Using Microsoft Excel, 5e © 2008 Pearson Prentice-Hall, Inc.Chap 8-1 Statistics for Managers Using Microsoft® Excel 5th Edition.
A NEW ECO TECHNOLOGY FOR FUNCTIONAL CHANGES AND REMOVING TIMING VIOLATIONS Jui-Hung Hung, Yao-Kai Yeh,Yung-Sheng Tseng and Tsai-Ming Hsieh Dept. of Information.
Regularity-Constrained Floorplanning for Multi-Core Processors Xi Chen and Jiang Hu (Department of ECE Texas A&M University), Ning Xu (College of CST Wuhan.
Ionic Conductors: Characterisation of Defect Structure Lecture 15 Total scattering analysis Dr. I. Abrahams Queen Mary University of London Lectures co-financed.
The Application of The Improved Hybrid Ant Colony Algorithm in Vehicle Routing Optimization Problem International Conference on Future Computer and Communication,
1 Chapter Two: Sampling Methods §know the reasons of sampling §use the table of random numbers §perform Simple Random, Systematic, Stratified, Cluster,
Introduction The berthing assignment problem requires that a detailed time-and-space-schedule be planned for incoming ships, with the goal of minimizing.
Dept. of Electrical and Computer Engineering The University of Texas at Austin E-Beam Lothography Stencil Planning and Optimization wit Overlapped Characters.
Statistical Power The power of a test is the probability of detecting a difference or relationship if such a difference or relationship really exists.
Chapter 7 Sampling Distributions Statistics for Business (Env) 1.
1 2 nd Pre-Lab Quiz 3 rd Pre-Lab Quiz 4 th Pre-Lab Quiz.
MINING COLOSSAL FREQUENT PATTERNS BY CORE PATTERN FUSION FEIDA ZHU, XIFENG YAN, JIAWEI HAN, PHILIP S. YU, HONG CHENG ICDE07 Advisor: Koh JiaLing Speaker:
March 23 & 28, Csci 2111: Data and File Structures Week 10, Lectures 1 & 2 Hashing.
March 23 & 28, Hashing. 2 What is Hashing? A Hash function is a function h(K) which transforms a key K into an address. Hashing is like indexing.
An Efficient Linear Time Triple Patterning Solver Haitong Tian Hongbo Zhang Zigang Xiao Martin D.F. Wong ASP-DAC’15.
Issues concerning the interpretation of statistical significance tests.
Confidence Interval Estimation For statistical inference in decision making:
Copyright © Cengage Learning. All rights reserved. 1 Functions and Models.
Chin-Hsiung Hsu, Yao-Wen Chang, and Sani Rechard Nassif From ICCAD09.
DNA Splicing Systems By William DeLorbe and Dr. Elizabeth Goode.
Evaluation of gene-expression clustering via mutual information distance measure Ido Priness, Oded Maimon and Irad Ben-Gal BMC Bioinformatics, 2007.
Geo479/579: Geostatistics Ch7. Spatial Continuity.
Organization of statistical investigation. Medical Statistics Commonly the word statistics means the arranging of data into charts, tables, and graphs.
October 1, 2013Computer Vision Lecture 9: From Edges to Contours 1 Canny Edge Detector However, usually there will still be noise in the array E[i, j],
Area/Sigma Notation Objective: To define area for plane regions with curvilinear boundaries. To use Sigma Notation to find areas.
Sampling Design and Analysis MTH 494 Lecture-21 Ossam Chohan Assistant Professor CIIT Abbottabad.
Revision Mid 1 Prof. Sin-Min Lee Department of Computer Science.
Measurements and Their Analysis. Introduction Note that in this chapter, we are talking about multiple measurements of the same quantity Numerical analysis.
Lecture 8: Measurement Errors 1. Objectives List some sources of measurement errors. Classify measurement errors into systematic and random errors. Study.
Conversation with one student After last week lecture, one student told me that he/she received a “poor” grade from the first lab. If you were the student,
1 IP Routing table compaction and sampling schemes to enhance TCAM cache performance Author: Ruirui Guo, Jose G. Delgado-Frias Publisher: Journal of Systems.
An unsupervised conditional random fields approach for clustering gene expression time series Chang-Tsun Li, Yinyin Yuan and Roland Wilson Bioinformatics,
Trigonometric Functions of Real Numbers 5. Trigonometric Graphs 5.3.
Area/Sigma Notation Objective: To define area for plane regions with curvilinear boundaries. To use Sigma Notation to find areas.
Copyright © Cengage Learning. All rights reserved. 4 Integrals.
One-Way Analysis of Variance
CONTINUOUS RANDOM VARIABLES AND THE NORMAL DISTRIBUTION
Presentation transcript:

Seongbo Shim, Yoojong Lee, and Youngsoo Shin Lithographic Defect Aware Placement Using Compact Standard Cells Without Inter-Cell Margin

Outline Introduction Defect probability computation Defect probability aware placement Experimental Results Conclusion

Introduction Any kind of pattern failure, e.g. contact bridge and metal short, originated from lithography process is called lithographic defect. Conventional standard cells contain extra space, called inter-cell margin, to prevent potential defects caused by lithography process. A margin is typically the width of a single poly pitch and a dummy poly is inserted in the margin so that polys can be regularly placed for better lithography.

Introduction Inter-cell margin, however, is not always necessary. In 28-nm technology we use in this paper, about 20% of cell pairs can be abutted together without margin but with zero defects, and about 30% of cell pairs with defect probability less than10%. This suggests a possibility of new library consisting of compact cells without inter-cell margin.

Introduction Some cells may be safely abutted for the benefit of reduction in area and wirelength; some others may be placed with extra whitespace in-between.

Defect probability computation A defect caused by lithography process is usually modeled by using process variation band (PVB) [1]. PVBs are obtained, after OPC are applied to layout, by repeated lithography simulation at various lithography settings.

Defect probability computation Defect probability can be modeled by the minimum distance between PVB pairs, called PVB distance.

Defect probability computation The defect probability when cells i and j are abutted together is modeled in a linear fashion and is given by Mpvb : PVB distance beyond which defect probability is 0% mpvb : PVB distance below which defect occurs in 100% PD(i,j): PVB distance between i and j. The values of Mpvb and mpvb are typically available from foundry fab

Defect probability computation Contact and metal 1 layers are the most critical layers for lithographic defect, so we measure D(i, j) on two layers and consider the larger one as its value.

Defect probability computation We assume 193nm ArF as an illumination source with immersion lithography in this paper. Optical influence range in this setting is about 1 μm. which reaches a few cells beyond cells A and B

Defect probability computation Lithography simulation turns out to take more than a minute. This is prohibitive because there are a million cell pairs for defect probability computation if a library contains 1000 cells Considering only abutted cells fortunately yields negligible amount of error in defect probability

Defect probability computation 2-pitch from boundary seems to be a reasonable choice, which we assume for fast computation of defect probability in this paper.

Defect probability computation A huge number of cell pairs still makes the computation intractable. We approach the problem by identifying the patterns along cell boundary and grouping them.

Defect probability computation Let a library contain 7 cells. Contact patterns within 2-pitch range from cell boundary, called extents.

Defect probability computation Note that extent, by its definition, can be abutted only along one side, which is denoted by thick line. Therefore, if there are n extent groups, there are different ways extents can be abutted. This is usually much smaller than the number of ways cells can be abutted,, where N is the number of cells and N > n.

Finally, cell pairs are mapped to corresponding extent pairs in which indicates the cell A with its orientation along y-axis flipped.

Defect probability computation Our 28-nm library consists of 1043 cells thus 2086 extents. They are grouped into 944 in contact layer, and 1117 in metal 1 layer. Defect probability computation in this case is expected to take and hours, respectively, which are much less than and hours when defect probability is computed for all cell pairs without any extent grouping.

Defect probability computation We try to reduce the number of extent groups by grouping similar (as well as exact) patterns together. In 90% of similarity, for example, two extents are grouped together if more than 90% area is filled with 0 once we form geometric XOR of the extents.

Defect probability computation We thus choose 90% similarity in our defect probability computation.

Defect probability computation D(i, j) have four different values because cells can be flipped along y-axis and the two cells can be abutted in two different orders. Defect probability table

Defect probability aware placement Many cell pairs have non-negligible defect probability when they are abutted, which should be carefully taken into account when cells are placed.

Defect probability aware placement A prototype tool has been developed based on simulated annealing to solve the problem [7], [8]. Each placement during simulated annealing is evaluated by using a cost function:

Defect probability aware placement : total wirelength measured by using half-perimeter of bounding box. : introduced to reserve enough amount of whitespace in each circuit row :corresponds to the average defect probability of all cell pairs

Defect probability aware placement : is the whitespace that is required to have defect probability RL(i): denotes the sum cell width : is the minimum RL(i) in the initial placement.

Defect probability aware placement The placement consists of two steps: (1) repeated placement using simulated annealing (2) whitespace distribution to further reduce defect probability Three operations are performed to generate a new placement for the first step.

Defect probability aware placement 1.Displaces a randomly picked cell (cell H) to a randomly chosen place cell overlap and inter-cell whitespace are removed accordingly

Defect probability aware placement 2. switches the location of randomly picked cell pairs (B and H).

Defect probability aware placement 3.cell (B) is picked, and its orientation along y-axis is flipped While the three operations are applied to generate a new placement, whitespace is allowed only at the right end of each circuit row. The second step of placement starts once the simulated annealing completes

Defect probability aware placement All cell pairs with the defect probability being larger than some threshold (the same threshold to control Cr) become the candidates whitespace of 1-poly pitch is distributed in greedy fashion.

Experimental Results

Conclusion Conventional standard cells contain inter-cell margin to prevent potential lithographic defects. We advocate that the margin can be removed for the benefit of chip area and wirelength, while defect probability is affected very little when cells are carefully places.