Dual Graph-Based Hot Spot Detection
description
Transcript of Dual Graph-Based Hot Spot Detection
Dual Graph-Based Hot Spot Detection
Andrew B. Kahng1
Chul-Hong Park2
Xu Xu1
(1) Blaze DFM, Inc.(2) ECE, University of California at San Diego
University of California, San Diego
Outline Introduction of Hot Spot Detection Dual Graph Based Approach Experimental Results Conclusions
University of California, San Diego
Why Hot Spot Detection?
Hot spots = features whose CD variation > T Form under a variety of conditions Reduce manufacturing yield Should be detected and solved in the early stage
Commercial tools: ORC (Mentor) and LRC (Synopsys)
Hot spot
University of California, San Diego
Previous Methods
Park et al. (SPIE 1999) proposed rule based detection with look-up tables Number of parameters increase for complex patterns
Speed merit of rule-based approach is reduced Inaccurate
Simulation-based approach has been a mainstream Detect hot spots accurately Hot spots can be changed according to process
conditions Model generations are significant overhead
Key Question Can we detect the hotspots fast and accurately?
University of California, San Diego
How We Think About Hot Spot Hotspot is a 2-dimensional function of line and space
with traditional parameters of DOF and Exposure Detect too many hot spots to classify the real hot spots
Our approach: more topological / graph-oriented
Practical methodology: Filter the chip layout down to a small candidate
set of hotspots, which can then be checked using the golden ORC/LRC tool
University of California, San Diego
(a) (b) (c)90
100
110
120
130
a b c
C-1 C-2 C-3
Nominal CD
Lithography Simulation
Different complexity leads to different CD variation CD variation is affected by different process condition More complex pattern, higher probability of hot spot
Probability: Pattern(c) > Pattern(b) > Pattern(a)
Simulation Condition: C-1: NA=0.85, σ=0.96/0.76, C-2: NA=0.75, σ=0.75/0.55, C-3: NA=0.75, σ=0.75/045DOF=0.2um, Exposure=+10% of nominal exposure
University of California, San Diego
Outline
Introduction of Hot Spot Detection Dual Graph Based Approach Experimental Results Conclusions
University of California, San Diego
Hot Spot Detection ProblemGiven: Layout L simulation conditions hot spot definitionDetect: Hot spots whose CD variation >T
To Minimize:
Number of un-detected and falsely detected hot spots
University of California, San Diego
“ Bad” Patterns Lead to Hotspots
Corner effect Proximity effectIn general, single effect does not lead to hot spots.
Hot spots are accumulative effects.
4 proximity effects, 2 corner effects
University of California, San Diego
Proposed Hot Spot Detection Flow Layout
Layout Graph Construction
Graph Planarization
Three-Level Detection
Local Pattern Density Filter
Output Hot Spots
University of California, San Diego
Layout Graph Construction
Corner effect
Proximity effect
Feature node
Two features with corner/proximity effects edge
University of California, San Diego
Edge Weighting Scheme Closed-form formula based approach
Weights of corner edges: constant Weights of proximate edges: f(w1, w2, l, d)= (w1’w2’l’) /d
Here w1’= w1 when w1 <c0
= c0 otherwise
Lookup table based
lw1
w2d
University of California, San Diego
Graph Planarization Delete one edge of any pair of crossing edges Convert the layout graph into its dual graph
(face dual node)
Planarization Dual graph
University of California, San Diego
Three-Level Hot Spot Detection For each edge
If (its weight > T0) report hot spot For each face (dual node)
If (the total weight > T1 ) report hot spot Sort all dual nodes according to weights Iteratively merge two dual nodes with max merged weight For each merged face (dual node)
If (the total edge weight > T2 )report hot spot
Edge Face Merged Face
University of California, San Diego
Local Pattern Density Filter
Hot spotNot Hot spot
Hot spots depend on the local pattern density A hot spots filtering based local pattern density
to reduce falsely detected hot spots
University of California, San Diego
Outline Introduction of Hot Spot Detection Dual Graph Based Approach Experimental Results Conclusions
University of California, San Diego
Experimental Setup Testcase: alu128 core
8.7K instances 90nm technology Chip size is 335 um X 285 um The netlists from OpenCores.
CalibreOPC , CalibreORC from Mentor Graphics are used for model-based OPC, and optical rule check (ORC)
Our algorithms are implemented in C++
University of California, San Diego
An Example of Hotspot Filtering
2D function (width, space) finds too many hotspots to classify the real hotspots
Real hotspot can be detected by dual graph based approach with weighted cost function
Detect hotspots which missed by rule-based approach Result is similar to simulation-based approach
(b) Hotspot(a) No Hotspot
University of California, San Diego
Experimental ResultsSimulation Condition
Number of Hot Spots Run time(s)ORC Detected False
DetectedORC Our
DOF=0.1ET=0.36
17 17 13 690 1.37
DOF=0.1ET=0.37
21 21 22 690 1.52
DOF=0.1ET=0.38
25 25 46 690 2.32
DOF=0.2ET=0.38
152 152 1291 690 4.38
Total 215 215 1372 2760 9.59 Runtime of our method is more than 287X faster compared to ORC Achieves 100% hot spot detection with small falsely defected hot spots overhead
University of California, San Diego
Outline Introduction of Hot Spot Detection Dual Graph Based Approach Experimental Results Conclusions
University of California, San Diego
Conclusion A novel fast dual graph based hot spot
detection algorithm Our method can detect hot spots with small
false detected overhead Runtime improvement is more than 287X
compared with ORC Future works
Fast hot spot detection engine in detailed router Cool spot detection: a pattern that is known to be
ORC/LRC-clean through the OPC
University of California, San Diego