Machine Learning Hotspot Prediction Significantly Improve

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Machine Learning Hotspot Prediction Significantly Improve Capture Rate on Wafer

Wei.YuanICRD/ASML 2020.11.06

SHANGHAI IC R&D CENTER上海集成电路研发中心有限公司

ICRD SINCE 2002 | 商密X级

Confidentialwww.icrd.com.cn

Outline

• Patterning Technology Roadmap

• Hotspot Prediction Background

• Machine Learning Hotspot Prediction

• Wafer Data Verification and Result Analysis

• Summary

2


Patterning Technology Roadmap

3

RBOPC

OPC

Illumination

Resist

Integration

Mask

≧ 45nmArF/ArF-iDUV/I-line

32/28nmArF-i

16/14nmArF-i

22/20nmArF-i

Binary

6% PSM

MBOPC

RBAF

PTD

Conventional

Annular

C-Quad

DipoleLEC

SMO

LELE DPT

SAV/SAC

SADP DPT

NTD

Wafer 3D

ILT

MBAF

APF

Tri-Layer

FinFET

OMOG

Mask 3D

10/7nmArF-i/EUV

MPTHigh-T PSM

Resist 3D

Machine learning OPC


Hotspot Prediction Background

• OPC verification(LMC) is playing the role after OPC correction to verify OPC quality.

• Key value of OPC verification is to distinguish risky patterns and safe patterns.

4

Killer defect

found in fab

Lithography/

Wafer processing• Problems are caught too late

• Cost of mask re-spin

• Longer time to good yield

Litho related

problems

Re-tune OPC

recipe

Mask Manufacturing

Without LMC

Litho related

problems

Recovers Lost

Cycle Time

LMC

Design RET/OPC

Data

Cycle time - days LMC saves >14 days of lost cycle time

LMC catches yield problems before reticle manufacturing

Design

completion End of Life

Lost

opportunity

$$$

Re

ve

nu

e (

$)

Time

Re-tune OPC

recipe

Printing

defect

predicted by

LMC

1st pass failed

inspection

2nd pass

good


Verification become more and more complex

5

CD/EP at NC/PW Less raw inputLess accuracy

More raw inputMore accuracy

Post OPC gdsand model Contour and image

ILS, DOF, MEEF,

PV band…

ML solution

• Traditional verification methods could not capture all mask related defects. Simple CD/EP inspection.

Cross condition inspections, image contrast inspections.

Solution: Construct more KPI; Introduce more accurate models.

• More complex but powerful solutions are required as process keep shrinking on.ML solution?

Mature nodes

Advanced nodes

Confidential

MACHINE LEARNING HOTSPOT PREDICTION


Newron Hotspot Prediction – Training Flow

• Image based defect defection flow constructed on DCNN.

• Makes full use of whole simulated images in order to generate prediction information.

• Multiple projecting routines are supported.To design space. Trained by huge amount simulation data.To behavior space. Trained by limited wafer data.

7

mask and

OPC model

Resist

image

Latency space

vector

CNN

network

Label from

wafer inspection

Design similarity

comparison

DNN

network

Model for behavior

space

Model for

design space

1) Generate resist image around hotspot location by

applying OPC model; Corp and covert whole resist

image to latency space vector via CNN.

2) Prepare the training data; ADI patterns with design

similarity comparison and labels (manually labeled ‘r/s’

for these ADI images by CDSEM value).

3) Feed training data into Newron engine to train two

separate prediction models.


Newron Hotspot Prediction – Application Flow

• Generate resist image, convert to latency space vectors for new hotspots.

• Latency space vectors are projected to new feature spaces by prediction models.

• Generate final prediction label by considering the two feature space distribution.

8

Latency

space vector

behavior space

design space

Hotspot candidates

in MTO LMC+ jobs

Verified

Hotspots

Prediction risky

or safe

In design space the Euclidean distance stands for the design similarity of one hotspot candidate to other hotspots in training set.

In behavior space the Euclidean distance stands for the probability of being risky or safe.

Central region of one hotspot image contribute more to the final similarity.

Ref Less similarMore similar

Confidential

WAFER DATA VERIFICATION AND RESULT ANALYSIS


• Total 375 unique hotspot candidates verified on wafer. (7 conditions*10shots*375=26250 measurements)

• KPI #1: CD variation in PW conditions. • The top 125 candidates with biggest CD variation --- “Risky”

• The rest 250 candidates with smallest CD variation --- “Safe”

• KPI #2: ADI model error at nominal condition.• The worst 125 candidates with biggest model error --- “Risky”

• The best 250 candidates with smallest model error --- “Safe”

Ground truth labeling based on real wafer data

10

Defocus -45 Defocus 45Nominal Condition

Hotspot A

CD variation 11.4nm

CD variation 1.53nm

Hotspot B


• Baseline ADI model has a good performance both on 1D and 2D patterns.

Baseline ADI model performance

11

14nm M1 NTD Model PerformanceSEM Prediction

TypeIn Spec

ratioOverall

RMSAnchor error

1D 96.7% 1.05

2D 99.8% 1.4

Total 1.24 -0.09


Use traditional detectors as baseline prediction solutions

• PVB-Focus has the most relevant physical meaning with through-focus band in wafer. Thus PVB detector shows best prediction accuracy.

12

Hotspot A

Hotspot B

0

0.25

0.5

0.75

Nuisance Rate Capture Rate F1

Prediction on KPI #1

PVB CD ILS NILS AI MEEF

Hotspot A

PredictionRisky Safe

Ground Truth

RiskyTrue Positive

(TP)False Negative

(FN)

SafeFalse Positive

(FP)True Negative

(TN)

Nuisance = FP

TP+FP, the lower the better

Capture = TP

TP+FN, the higher the better

F1= 2

Capture−1+(1−Nuisance)−1, the higher the better

Hotspot B

（simulation CD）

（w

afe

r C

D）


Newron hotspot prediction result comparison

• Risky and safe hotspots show clear separation in feature space.

• Final F1 score get ~47% improvement in KPI#1.

13

Although some safe hotspot are mixed with risky hotspot in test set, the overall distribution matched well to train set.

Hotspot A

Hotspot B 0.31

0.43

0.53

0.33

0.91

0.78

0

0.25

0.5

0.75

1

Nuisance Capture F1


PVB

Newron

+47%+111%


Newron can handle more complex predictions

• Traditional detectors do not have the capability to predict the model error because all the detectors use the information simulated by the model itself.

14

Model error

There's no obvious correlation between NC model error and PVB-Focus, MEEF or ILS.


Newron can handle more complex predictions

• Resist image contain sufficient info for such kind of KPI；

• Machine learning solution can achieve good enough prediction.

15

Newron solution result on model error at NC.

Capture the model error by ML method.

0.12

0.68

0.78

0

0.25

0.5

0.75

1

Nuisance Capture F1


Model error


Summary

• ICRD and ASML-Brion co-worked to evaluate an innovative machine learning hotspot

prediction method on 14nm metal layer process.

• Traditional detectors are insufficient to predict wafer hotspots for advanced tech node.

• To predict the ground truth labeled by CD variation in PW conditions, Newron Hotspot

improves the prediction F1 score by 47% compared with baseline detectors.

• Newron Hotspot achieved a breakthrough in predicting model error with 68% capture

rate and 12% nuisance rate while there is no traditional solution yet.

• It has been proved that Newron Hotspot flow can be flexibly adjusted to handle

various prediction targets.

16


More Study on ML

17

ML SRAF(IWAPS 2019)

ML OPC（Continued ）

ML OPC（SPIE 2019）

Machine Learning

ML hotspot Prediction

(IWAPS 2020)

ML ADI model（SPIE 2019）

ML etch model（SPIE 2020）

ML ADI model

ML AEI model ML OPC

ML SRAF

• Machine Learning technologies have high potential to be applied in OPC area and will bring significant benefit to improve the OPC performance.


Acknowledgement

• Thank ICRD OPC team and ASML-Brion LMC/MKT team’s great support and co-work to finish this study.

18

Wei Yuan

Yifei Lu

Ming Li

Bingyang Pan

Ying Gao

Yu Tian

Zhi-qin Li

Liang Ji

Ying Huang

Hao Chen

Yueliang Yao

Yanjun Xiao

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Machine Learning Hotspot Prediction Significantly Improve