VLSI Mask Optimization:From Shallow To Deep Learning
Haoyu Yang1, Wei Zhong2, Yuzhe Ma1, Hao Geng1,Ran Chen1, Wanli Chen1, Bei Yu1
1The Chinese University of Hong Kong2Dalian University of Technology
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Moore’s Law to Extreme Scaling
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A10 A11
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Intel MicroprocessorsApple Microprocessors
2 / 22
Challenge 1: Failure (Hotspot) Detection
Pre-OPC Layout Post-OPC Mask Hotspot on Wafer
I RET: OPC, SRAF, MPLI Still hotspot: low fidelity patternsI Simulations: extremely CPU intensive
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3 / 22
Challenge 2: Optical Proximity Correction (OPC)
Design target Mask Wafer
without OPC
with OPC
4 / 22
Why Deep Learning?I Feature Crafting v.s. Feature Learning
Although prior knowledge is considered during manually feature design, informationloss is inevitable.Feature learned from mass dataset is more reliable.
I ScalabilityWith shrinking down circuit feature size, mask layout becomes more complicated.Deep learning has the potential to handle ultra-large-scale instances while traditionalmachine learning may suffer from performance degradation.
I Mature Libraries
5 / 22
Outline
Hotspot Detection via Machine Learning
OPC via Machine Learning
Heterogeneous OPC
6 / 22
Outline
Hotspot Detection via Machine Learning
OPC via Machine Learning
Heterogeneous OPC
7 / 22
Hotspot Detection Hierarchy
Increasing verificationaccuracy
Sampling
Hotspot Detection
Lithography Simulation
(Relative) CPU runtime at each level
I Sampling (DRC Checking):scan and rule check each region
I Hotspot Detection:verify the sampled regions and report potential hotspots
I Lithography Simulation:final verification on the reported hotspots
7 / 22
Early Study of DNN-based Hotspot Detector∗
I Total 21 layers with 13 convolution layers and 5 pooling layers.I A ReLU is applied after each convolution layer.
…
…Hotspot
Non-Hotspot
512x512x4256x256x4
256x256x8
128x128x16128x128x8
64x64x16 64x64x3232x32x32 32x32x32
16x16x32
2048
512
C1
P1 C2-1C2-2 C2-3
P2 C3-1 C3-2 C4-1P3C3-3 C4-2 C4-3
C5-1 C5-2 C5-3P4 P5
∗Haoyu Yang, Luyang Luo, et al. (2017). “Imbalance aware lithography hotspot detection: a deep learningapproach”. In: JM3 16.3, p. 033504.
8 / 22
What Does Deep Learning Learn?
Origin Pool1 Pool2
Pool3 Pool4 Pool5
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The Biased Learning Algorithm [DAC’17]†
Training Set
Update ε
yh=[0,1]
yn=[1-ε, ε]
MGD:
end-to-end
training
Stop Criteria
Trained Model
Yes
No
80 82 84 86 88 902,000
3,000
4,000
Accuracy (%)Fa
lseAlarm
Shift-Boundary Bias
†Haoyu Yang, Jing Su, et al. (2017). “Layout Hotspot Detection with Feature Tensor Generation and DeepBiased Learning”. In: Proc. DAC, 62:1–62:6.10 / 22
Optimizing AUC [ASPDAC’19]‡
The AUC objective:LΦ(f ) = 1
N+N−
∑N+
i=1∑N−
j=1 Φ(
f(x+
i)− f
(x−j))
.
Approximation candidates:
PSL ΦPSL(z) = (1− z)2
PHL ΦPHL(z) = max(1− z, 0)
PLL ΦPLL(z) = log(1 + exp(−βz))
R ΦR∗(z) =
{−(z− γ)p, if z > γ0, otherwise
‡Wei Ye et al. (2019). “LithoROC: lithography hotspot detection with explicit ROC optimization”. In:Proc. ASPDAC, pp. 292–298.
11 / 22
Conventional Clip based Solution
Region
…
Conventional Hotspot Detector
Hotspot
Non-Hotspot
Clips
I A binary classification problem.I Scan over whole region.I Single stage detector.
I Scanning is time consuming and single stage is not robust to false alarm.
12 / 22
Region based approach [DAC’19]
Region
Hotspot Core
Region-based Hotspot Detector
Feature Extraction
Clip Proposal Network
Refinement
I Learning what and where is hotspot at same time.I Classification Problem -> Classification & Regression Problem.
Ran Chen et al. (2019). “Faster Region-based Hotspot Detection”. In: Proc. DAC, 146:1–146:6.13 / 22
Outline
Hotspot Detection via Machine Learning
OPC via Machine Learning
Heterogeneous OPC
14 / 22
OPC Previous Work
Classic OPCI Model/Rule-based OPC
[Cobb+,SPIE’02][Kuang+,DATE’15][Awad+,DAC’16][Su+,ICCAD’16]1. Fragmentation of shape edges;2. Move fragments for better printability.
I Inverse Lithography[Pang+,SPIE’05][Gao+,DAC’14][Poonawala+,TIP’07][Ma+,ICCAD’17]1. Efficient model that maps mask to
aerial image;2. Continuously update mask through
descending the gradient of contourerror.
Machine Learning OPC[Matsunawa+,JM3’16][Choi+,SPIE’16][Xu+,ISPD’16][Shim+,APCCAS’16]
1. Edge fragmentation;2. Feature extraction;3. Model training.
14 / 22
Machine Learning-based SRAF InsertionSRAF Insertion with Machine Learning [ISPD’16]¶
Label: 1
Label: 0
0 1 2N%1sub%sampling0point
Tackling Robustness with Dictionary Learning [ASPDAC’19]‖
n+
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¶Xiaoqing Xu et al. (2016). “A machine learning based framework for sub-resolution assist featuregeneration”. In: Proc. ISPD, pp. 161–168.
‖Hao Geng et al. (2019). “SRAF Insertion via Supervised Dictionary Learning”. In: Proc. ASPDAC,pp. 406–411.
15 / 22
Machine Learning Assists Model-based OPC [ASPDAC’19]∗∗
I Replace lithography simulation (slow) with machine learning-based EPE predictor(fast) in OPC iterations.
∗∗Bentian Jiang et al. (2019). “A fast machine learning-based mask printability predictor for OPCacceleration”. In: Proc. ASPDAC, pp. 412–419.
16 / 22
GAN-OPC [DAC’18]††
…
0.2 0.8
BadMask
GoodMask
Discrim
inator
Generator
…
EncoderD
ecoder
Target
Mask
Target & Mask
I Better starting points for legacy OPC engine and reduce iteration count.††Haoyu Yang, Shuhe Li, et al. (2018). “GAN-OPC: Mask Optimization with Lithography-guided Generative
Adversarial Nets”. In: Proc. DAC, 131:1–131:6.17 / 22
Outline
Hotspot Detection via Machine Learning
OPC via Machine Learning
Heterogeneous OPC
18 / 22
An Observation of Previous OPC SolutionsMachine learning solutions rely on legacy OPC engines
Generator RealFake
Generator
(a)
(b)
Feed-forward Back-propagetion
Discriminator
Litho-Simulator
Legacy OPC engines exhibit different performance on different designs
1 2 3 4 5 6 7 8 9 1002468·104
Design ID
MSE
MB-OPCILT
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A Design of Heterogeneous OPC Framework
Classification Model
ILT
MB-OPC
Mask
MaskDesignDesigns
OPC-1
OPC-N
Masks
Masks
… …
We design a classification model that can determine the best OPC engine for a givendesign at trivial cost.
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Training on Artificial DesignsI Training data comes from GAN-OPC and is labeled according to results of MB-OPC
and ILT.I Test on 10 designs from ICCAD 2013 CAD Contest.
0 20 40 60 80 1000.00
20.00
40.00
60.00
80.00
100.00
Epoch
Accuracy
(%)
Training Testing
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Experimental Results
1 2 3 4 5 6 7 8 9 100
2
4
6
8·104
Design ID
MSE
MB-OPCILT
HOPC
Several BenefitsI Does not require extremely high prediction accuracy of the classification model.I Take advantages of different OPC solutions on different designs.
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Conclusion and Discussion
So Far:I Recent progress of deterministic machine learning model for hotspot detectionI State-of-the-art machine learning solutions for OPC and SRAF insertionI A heterogeneous OPC framework guided by a classification engine
Future:I Manufacturability issues.I Classification challenge when more than two OPC engines are available.
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