Yield & Process Improvement by Data Analysis along the ... · Keywords are Fault Detection and...

Roberto RappDirector Manufacturing Process Integration

Semiconductor & MEMS Plant Reutlingen

Robert Bosch GmbH

Yield & Process Improvement by Data Analysis along the Supply Chain

SEMICON EUROPA MunichNov. 14th, 2018

Automotive Electronics | Roberto Rapp | 10/31/2018© Robert Bosch GmbH 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.

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SEMICON EUROPA Munich, 11/14/18Abstract

Along the semiconductor supply chain an increasing amount of production data is generated and waiting to be used for continuous process-, yield- and quality-improvements. To classic Statistical Process Control (SPC) and data analysis, done by engineers, a variety of new applications were added over the years. Keywords are Fault Detection and Classification (FDC), Automatic Defect Classification (ADC), Advanced Process Control (APC), data lakes, data mining - just to name a few.

Looking at the supply chain from wafer start, up to electrical wafer test (EWS) traceability is a given. Other than ASICs many MEMS elements can’t be traced back to, like wafer number or x/y position. So it is important to make use of data from different steps in production.

In this presentation examples for automatic analysis of inline defectivity and EWS are shown. Furthermore the successful implementation of traceability, from final product test of MEMS sensors to inline production data is discussed.


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SEMICON EUROPA Munich, 11/14/18Agenda1. Introduction – A Sensor’s Perspective

2. Data Analysis along the Supply Chain

• Use of Defectivity Data (single process)

• Outlier Detection in Electrical Wafer Sort and Final Test

• Combination of Inline Defectiviy Data with EWS

• ASIC to MEMS Mapping

• A brief outlook

SEMICON EUROPA Munich, 11/14/18

Automotive Electronics Roberto Rapp | 10/31/2018© Robert Bosch GmbH 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.

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Megatrends of the Future

Electrified Automated Connected and IoT

Power Semiconductor

System-ICs

System-ICs IP

Automotive Sensors CE-Sensors

Micro-optical Systems



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MEMS Main Automotive ApplicationsAcceleration Sensors GyroscopesPressure SensorsPressure Sensors Acceleration Sensors Gyroscopes

Engine Management

Side Airbag Airbag

Vehicle Dynamics Control

Airbag

Vehicle Dynamics Control

Standard equipment



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Sensors for automated driving – a MEMS perspective10

Sen

sors 1 Mass flow sensor

1 Pressure sensor [Barometric air pressure]2 Pressure sensors [Manifold air pressure, oil]1 High pressure sensor [Common Rail]1 Pressure sensor [Tank pressure]1 Pressure sensor [Start/stop function]2 Acceleration sensors [Active engine mounting]1 Pressure sensor [Diesel particulate filter]

Engine Management e.g. Diesel

27 S

enso

rs 2 High-g acceleration sensors [Airbag]1 Angular rate sensor, 1 Low-g acceleration sensor [Roll-over sensing], 1 Acceleration sensor (Structure-borne sound sensor) [Airbag] 4 Acceleration sensors, 2 Pressure sensors [Peripheral airbag sensors]2 Pressure sensors [Pedestrian safety]1 Angular rate sensor, 1 Low-g acceleration sensor, 1 High pressure sensor [ESP (incl. ACC)]1 Angular rate sensor [Active steering] 1 Acceleration sensor [eCall]4 Pressure sensors, 4 acceleration sensors [TPMS]1 Pressure sensor [Occupant detection]

17 S

enso

rs 2 Pressure sensors [Automatic transmission] 5 Acceleration sensors [Active suspension] 1 Pressure sensor, 1 Humidity sensor, 2 Gas sensors [Air conditioning, air quality]1 Angular rate sensor, 1 Acceleration sensor [Navigation] 3 Microphones [telephone]1 Bolometer Array [Night vision] 1 Acceleration sensor [Car alarm](Seldom: 16 Pressure sensors (up to 8 pressure sensors per seat)

Comfort

Safety

… high-performance IMU… µMirror for head lamps... µMirror for Lidar… human-machine interface… driver monitoring… connectivity

HAD


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SEMICON EUROPA Munich, 11/14/18Agenda1. Introduction – A Sensor’s Perspective

2. Data Analysis along the Supply Chain

• Use of Defectivity Data (single process)

• Outlier Detection in Electrical Wafer Sort and Final Test

• Combination of Inline Defectiviy Data with EWS

• ASIC to MEMS Mapping

• A brief outlook



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Value stream for semiconductor plant Reutlingen

Wafer-Fab

MSBVM

MEMSFab

MSBFinaltest

1 x tägl. 7 Tagewoche

Rohwafer

IEingang

Die-Bank

IEingang

IEingang


LOP

Assembly

Chips

LOP

RBACFinaltest

MSBAssembly

Eingang

I

RBHHAssembly

Foundry

Eingang

IWafer-Fab

MEMSFab

WaferTest

Assem-bly

Final-Test

HatvanAssem-

bly

ChinaFinal-Test

LOG LOG ENGENGENG

ExternalAssembly

ExternalFoundry

RB Reutlingen

RB worldwide

Supplier worldwide

DeliveryCustomer


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SEMICON EUROPA Munich, 11/14/18Use of data in Wafer FabFlow of Defect-Inspektion with SPACE and eCAP


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SEMICON EUROPA Munich, 11/14/18Use of data in Wafer FabAutomated Inline Reaction on defect types by Space

Process StepInspection + review+ signature analysis SPC + eCAP

Process & Equipment

Tool 1:

Tool 2:

Classification by- Inspection tool RTB- SPR- ADC/operator

Tool 1 Chamber1

Tool 1 Chamber2

Tool 2 Chamber1

Tool 2 Chamber2

SPC Concept

eCap (electronic Control action plan):- Immediate reaction on tool and chamber level- disposition of affected material- Maintenance and requalification

SPAC

E - G

atew

ay

SPAC

E - F

ram

ewor

k

Regelkarte

Specifi-cation

Database

SPC-Violation

SPC-Violation

SPC-Violation

Request

Reply

Event

Event

Event

Regelkarte

Regelkarte

SPACE eCAP

Chamber 1

Chamber 2

Chamber 1

Chamber 2

Input: DD Data

Output: OCAP on chamber level



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Use of data for quality improvement in EWS and FTDetection of outliers and defects

Outlier within specification Good dies in bad neighbourhood

© Taino Palermo

© Pintail Technologies

© Olaf Engel, Wuppertal; www.muehlstein-online.de



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Outlier removal as a method of quality improvement General Considerations

Schematic of Outliers

Example: parametric test result wafer test

Part Average Testing (PAT)Outliers: parts with abnormal characteristic with respect to a main distribution

Outliers can potentially fail in field even though they were tested in spec

AEC-Q001 provides description for statistical calculation of PAT limits and outlier removal

Lot to lot variations require introduction of dynamic PAT limits: yield vs efficiency

Part Average Test Limits and Outliers

Outliers Main Distribution Spec Limits PAT Limits

upper spec limit

lower spec limit

outlier

Use of data in in EWS & FTStatistical BIN Limits & Part Average Test



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Testing

4s limit based on last 6 months of production

Outlier

Statistical BIN Limits

OCAP for shop floor

Lot dispatching, further measures

Analysis in expert team

Testing

Part Average Test / Parametric BOPP

Lot no. Fail

rate

OutlierPart no. Te

st re

sult

Dynamic PAT limits based on test results

Scrap bad dies, inform engineering if fail rate > threshold

Para

met

er a

Parameter b

OutlierBOPP Correlations



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Use of data in EWS: Bosch Post Processing, geometrical Application

Scratch detectionElectrical failures in grayOutlier classification:• Black: expansion of cluster• Yellow: position with reduced yield for most wafers of lot• Pink: Scratch detection• Red: electrical outliers • Orange: electrical outliers (compared to neighborhood)• Blue: small electrical outliers in several tests

Removal of good dies in bad environment

Expansion of cluster

Position with reduced yield for most wafers in lot

Statistical BIN Limits & Part Average TestExamples from Final test and Customer data

Part Average TestStatistical BIN Limits



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Outliers

Damaged bond wires

Root cause:damaged lead framepusher

Failure analysis revealed damaged bond wiresRoot cause found in Assembly

BIN trend increase in fail frequency detected

Failure analysis showed a crack in the MEMS elementEffectiveness of the selected limits is proven

Particle in MEMS found

Evaluation of score limits in addition to outlier limits- Acceleration sensor exceeded score limits in multiple tests- Accumulation of scores leads to scrapping of the tested device

Crack



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Wafer-Fab

MSBVM

MEMSFab

MSBFinaltest


Rohwafer

IEingang

Die-Bank

IEingang

IEingang


LOP

Assembly

Chips

LOP

RBACFinaltest

MSBAssembly

Eingang

I

RBHHAssembly

Foundry

Eingang

IWafer-Fab

MEMSFab VM

Assem-bly

Final-Test

HatvanAssem-

bly

ChinaFinal-Test

LOG LOG ENGENGENG

ASIC to MEMS Mapping (A2MM)

ASIC-2-MEMS-MappingExternal

Assembly

ExternalFoundry

CustomerDelivery

Saw

ing



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A2MM: Data Analytics in worldwide value streamDelivery

Customer



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1. Data for Volume and Traceability2. Data for Product- and Process-Quality3. Data Generation 4. Data Security

Data within the supply chain – “Outlook”

Semicon 2018 Munich, 11/14/18


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Abbreviations ADC: Automatic Defect Classification ASIC: Application-Specific Integrated Circuit APC: Advanced Process Control BOPP: Bosch Post Processing (Software) ECAP: Electronic Control Action Plan FDC: Fault Detection and Classification HAD: Highly automated driving OCAP: Out of Control Action Plan MES: Manufacturing Execution System MEMS: Micro-Electro-Mechanical System PAT: Part Average Test RTC: Real Time Classification SPC: Statistical Process Control SPR: Spatial Pattern Recognition

1. SC Management Meeting

Dr. Dirk Hoheisel | 10/31/2018© Robert Bosch GmbH 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.

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Yield & Process Improvement by Data Analysis along the ... · Keywords are Fault Detection and...

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