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© Fraunhofer IISB, 11-2018
Predictive Probing:A novel approach to minimize efforts at final test
SEMICON Europa 2018TechARENA: Metrology for Emerging Technologies
Dr.-Ing. Martin SchellenbergerGroup Manager Equipment & APCFraunhofer IISB, Erlangen, [email protected]
© Fraunhofer IISB, 11-2018
© Fraunhofer IISB, 11-2018
Fraunhofer IISB
Schottkystraße 1091058 Erlangen
www.iisb.fraunhofer.de
© Fraunhofer IISB, 11-2018
Predictive Probing: A novel approach to minimize efforts at final testTechARENA: Metrology for Emerging Technologies
I. Some (rather limited) History, Part I: Integrated Metrology
II. History, Part II: „Data Analytics“ enters the metrology domain
III. The art of Predictive Probing
IV. Summary
© Fraunhofer IISB, 11-2018
Predictive Probing: A novel approach to minimize efforts at final testTechARENA: Metrology for Emerging Technologies
I. Some (rather limited) History, Part I: Integrated Metrology
II. History, Part II: „Data Analytics“ enters the metrology domain
III. The art of Predictive Probing
IV. Summary
© Fraunhofer IISB, 11-2018
In situ spectroscopic ellipsometry in a batch furnace
Layout of the batch furnace Prism-based optical system for the in situellipsometry measurement
Realtime control of oxide growth
1990+
© Fraunhofer IISB, 11-2018
In-line X-ray Photoelectron Spectroscopy in a cluster tool
Transfer
and
Control
Cluster Tool
Wafer
Processing
Surface
control
by XPS
1990+
In-line control of layerproperties right after processing
© Fraunhofer IISB, 11-2018
In situ optical emission spectroscopy in a batch furnace
Real-time control of plasma processes by integrated OES
8
2010+
Again: Prism-based optical system
© Fraunhofer IISB, 11-2018
Predictive Probing: A novel approach to minimize efforts at final testTechARENA: Metrology for Emerging Technologies
I. Some (rather limited) History, Part I: Integrated Metrology
II. History, Part II: „Data Analytics“ enters the metrology domain
III. The art of Predictive Probing
IV. Summary
ado
pte
dfr
om
gart
ner
.co
m
© Fraunhofer IISB, 11-2018
Measurement system mounted on a
single-wafer FOUP with adapter
Pic
ture
by c
ourt
esy o
f In
fin
eon,
Dre
sden
Stocker-integrated metrology for CD control 2000+
Measure intensity
as a function of the
azimuth angle
Diffraction signature
Fault detection:
Good / Bad
-40 -30 -20 -10 0 10 20 30 400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Azimut-Winkel (°)
Inte
nsi
taet
(a.u
.)
64M-DT-L-alle-s2s23-p8p40p72: Beugungssignaturen, Pol. 1, sortiert nach WAFNR
Azimuth angle (°)
misprocessed
wafers
Fault detection and classification (FDC) based on neural networks
© Fraunhofer IISB, 11-2018
Relevant data are continuously collected and analyzed using a Bayesian network.
The network predicts the real time remaining until the break of the filament with an accuracy of 10-20 hours.
Thus, no “mere” preventive maintenance after a predetermined operating time or number of processes needs to be carried out, and no system failures are risked by missed maintenance steps.
Predictive maintenance in ion-implantation 2010+
Based on these forecasts, maintenance tasks can be scheduled exactly.
© Fraunhofer IISB, 11-2018
Virtual metrology for deep-trench etching 2010+
Relevant data are continuously collected and evaluated using a “gradient boosting tree” algorithm.
The algorithm predicts the actual depth of the trench after the etching process with a deviation of less than 4 nm compared to values obtained from physical metrology.
Regular, costly and time-consuming physical measurements can be limited.
The application of virtual metrology allows “virtual” control of every single wafer
© Fraunhofer IISB, 11-2018
Predictive Probing: A novel approach to minimize efforts at final testTechARENA: Metrology for Emerging Technologies
I. Some (rather limited) History, Part I: Integrated Metrology
II. History, Part II: „Data Analytics“ enters the metrology domain
III. The art of Predictive Probing
IV. Summary
© Fraunhofer IISB, 11-2018
Predictive ProbingUse-case: LED manufacturing
Several process steps, e.g.:
▪ Epitaxy to create the optically active layers
▪ Doping to achieve certain electrical properties
▪ Metallization to generate contacts
▪ Layer formation to guide the emitted light
Quality control, e.g.:
▪ Particle measurement
▪ Ultrasonic measurements
▪ Photoluminescence measurements
▪ Probing at wafer level
▪ Final test
Epitaxy Processing Packaging Light Engine
Up to 50% of total cost
© Fraunhofer IISB, 11-2018
100% Probing – electrical and optical measurement of every single chip:
▪ Electrical and optical properties
▪ Defect chips
Time-consuming and expensiveProbing Probing result
Predictive ProbingState-of-the-art probing in LED manufacturing
© Fraunhofer IISB, 11-2018
Predictive Probing
▪ Reduced probing of a selected number of chips
▪ Based on data analysis – not on mere random or statistical reduction
(reduced)
Predictive Probing
Same result, but: partly measured, partly reconstructed
full probing
Predictive ProbingThe concept
© Fraunhofer IISB, 11-2018
Predictive ProbingConstruction of the probing map
Optical and electrical properties:
▪ Basic test grid based on analysis of historical probing data
© Fraunhofer IISB, 11-2018
Optical and electrical properties:
▪ Basic test grid based on analysis of historical probing data
Defects:
▪ Analyse measurements prior to probing
▪ Individually calculate specific defect test grid for every wafer
Predictive ProbingConstruction of the probing map
© Fraunhofer IISB, 11-2018
Predictive ProbingNew probing approach and result
Two-step Predictive Probing process:
1. Analyse prior measurements, compile probing map to determine electrical and optical properties as well as defects and probe selected LED-chips
© Fraunhofer IISB, 11-2018
Two-step Predictive Probing process:
1. Analyse prior measurements, compile probing map to determine electrical and optical properties as well as defects and probe selected LED-chips
2. Read measurements, interpolate optical and electrical values and mark defect LED-chips
Predictive ProbingNew probing approach and result
© Fraunhofer IISB, 11-2018
Results
▪ Accurate interpolation of LED properties
▪ Defect detection accuracy meets application requirements, almost always ...
Two-step Predictive Probing process:
1. Analyse prior measurements, compile probing map to determine electrical and optical properties as well as defects and probe selected LED-chips
2. Read measurements, interpolate optical and electrical values and mark defect LED-chips
Predictive ProbingNew probing approach and result
© Fraunhofer IISB, 11-2018
Predictive ProbingImproving defect detection
… but not for wafers with edge voids:
▪ A small percentage of wafers show edge voids
▪ No accurate detection with traditional analysis methods possible, only work-around solutions
▪ Visible for the human eye in photoluminescence measurements, though
© Fraunhofer IISB, 11-2018
Predictive ProbingEdge Void Classification
Challenges:
▪ Measured brightness varies highly – so do edge void shapes and sizes
▪ Every single chip (>130,000) must be classified
photoluminescence measurements
© Fraunhofer IISB, 11-2018
Edge Void ClassificationSolution approach
Fully Convolutional Networks:
▪ Based on a special network architecture for computer vision
▪ Self-learning algorithm for pixel-wise classification
© Fraunhofer IISB, 11-2018
Edge Void Classification Some intuition about fully convolutional networks
Fully Convolutional Networks:
▪ Vanilla (regular) neural network process vectorised data
▪ Computer vision networks, by contrast, preserve spatial information by filtering the image
© Fraunhofer IISB, 11-2018
Filters: feature detectors that are robust against rotation, scale and translation variance
fur structure
cat eyes
cat ears
cat nose
fur colour
paws
whiskers
cat mouth
fur pattern
Edge Void Classification Some intuition about fully convolutional networks
© Fraunhofer IISB, 11-2018
edge void
void
chips ok
edge void
voids
void
Edge Void Classification Some intuition about fully convolutional networks
© Fraunhofer IISB, 11-2018
▪ A typical network contains thousands of filters, allowing the classification of highly variant images
▪ With increasing network depth filters are getting more complex
incr
easi
ng
net
wo
rk d
epth
https://storage.googleapis.com/deepdream/visualz/vgg16/index.html
“cat
”
Edge Void Classification Some intuition about fully convolutional networks
© Fraunhofer IISB, 11-2018
▪ The network’s performance is to learn suitable filters for the given classification task
▪ Therefore the network has to be trained with a carefully assembled dataset of inputs and corresponding labels
https://distill.pub/2017/feature-visualization/
network training progress
randomly initialised filter trained filterearly training advanced training
Edge Void Classification Some intuition about fully convolutional networks
© Fraunhofer IISB, 11-2018
Edge Void ClassificationFully Convolutional Networks
Network training:
▪ Input: about 100 photoluminescence measurements
▪ Labels: 3 prediction classes - chips ok / background / defect chips
© Fraunhofer IISB, 11-2018
Edge Void ClassificationResults
Predictive probing defect detection accuracy significantly improved
▪ Over 98.5 % of all 168,100 pixels correctly classified
▪ Independent of wafer / chip size
▪ Network knowledge transferrable to other pattern recognition tasks
© Fraunhofer IISB, 11-2018
Predictive Probing: A novel approach to minimize efforts at final testTechARENA: Metrology for Emerging Technologies
I. Some (rather limited) History, Part I: Integrated Metrology
II. History, Part II: „Data Analytics“ enters the metrology domain
III. The art of Predictive Probing
IV. Summary
© Fraunhofer IISB, 11-2018
Predictive Probing to reducetime and cost for device test
▪ Relevant upstream data are continuously collected and analyzed to predict device properties without actually measuring them
▪ Basis: long-term/short-term historic data, e.g. from upstream measurements
▪ Plus: Fully convolutional neural network
▪ Accurate interpolation of LED properties with ~7% measured chips.
▪ Benefit: significant time and cost savings
Summary IPredictive Probing in LED final test
LED-chips not to be measured
LED-chips to be measured
© Fraunhofer IISB, 11-2018
Artificial intelligence (AI) will play an inevitable roleas enabler for “Metrology for Emerging Technologies”
▪ AI is the next step, amending “integration” and “data analytics”
▪ Semiconductor (SC) industry deals with a complex environment for production and quality control
▪ SC industry already gained a lot of experience in the AI domain
▪ Predictive maintenance, virtual metrology
▪ Combine analytics with domain knowledge (no “mere” informatics)
▪ Follow closely other AI domains!
▪ Especially in the area of big data analytics and deep learning
Summary IITechARENA: Metrology for Emerging Technologies
© Fraunhofer IISB, 11-2018
Acknowledgement
Parts of the work presented here was funded within
- IMPROVE (ENIAC Joint Undertaking)
- SEAL (EU Seventh Framework Programme)
- EPPL (ENIAC Joint Undertaking)
- INTEGREAT (German BMBF)
© Fraunhofer IISB, 11-2018
Thank you for your interest!
Dr.-Ing. Martin SchellenbergerGroup Manager Data AnalyticsFraunhofer IISB, Erlangen, [email protected]