Manufacturing Test Challenges for IoT

and Automotive Market Segments

Roberto Lissoni

ST Corporate Quality Director

Who We Are 2

• Approximately 43,200 employees worldwide

• Approximately 8,300 people working in R&D

• 11 manufacturing sites

• Over 75 sales & marketing offices

• A global semiconductor leader

• 2015 revenues of $6.90B

• Listed: NYSE, Euronext Paris

and Borsa Italiana, Milan

Front-End

Back-End

Research & Development

Main Sales & Marketing

As of December 31, 2015

Flexible and Independent Manufacturing 3

Front-End

Back-End

Morocco

France

(Crolles, Rousset, Tours)

Italy

(Agrate, Catania)

Malaysia

Singapore

Philippines

China

(Shenzhen)

Malta

Our Vision 4

ST stands for

Everywhere

microelectronics make a

positive contribution to

people’s lives, ST is

there

Application Strategic Focus 5

Safer

Greener

More

connected

Smart

Industry

Smart

CitySmart

Things

The leading provider of products and solutions

for Smart Driving and the Internet of Things

Smart

Home

Discrete &

Power

Transistors

Dedicated

Automotive ICs

Analog, Industrial &

Power Conversion

ICs

Product Family Focus 6

The leading provider of products and solutions

for Smart Driving and the Internet of Things

Portfolio delivering complementarity for target end markets, and synergies in R&D and manufacturing

Digital

ASICs

General Purpose &

Secure MCUs

EEPROM

MEMS &

Specialized

Imaging Sensors

Presentation Outline

• ST the context

• The challenges from the served market

• The solutions enabled by O+

• Look ahead: next challenges

7

ST

• ST is a global semiconductor company

• ST is serving two main segment:

• IoT

• Automotive

• ST is owning overall core process:

• Product and technology development

• Diffusion

• Assembly

• Testing

8

Main challenges from the served market

• IoT:

• Vertical Ramp Up Time to Quality

• Efficiency in manufacturing --> Testing flexibility to manage vertical changes in the demand

• Automotive:

• Quality performance PPB

• Outlier detection

• Zero Quality Excursion need a solid Excursions Eradication program

• Overall: efficient, effective and flexible testing infrastracture with «real time»

performance management

9

Testing: a Strategic Asset to increase Company Perfomance

• A company level strategy based on:

• A company vision on test strategy and test needs

• A company wide program (TEIP) to streamline:

• Test data storage, retrieve and data architecture

• Test Platform

• Test Capacity Model

• Test Efficiency and Effectiveness focused programs

• A company level program to:

• Break the Silos due to organization structure

• Focus resources and competences

• Converge customer needs with proper test solutions in terms of:

• Efficiency capacity optimization, demand management

• Effectiveness Quality Firewall

10

Increased Efficiency

• Streamlined data flow and data architecture O+ Solution

• O+ solution enabled:

• Real time status of testers and testing performance

• Weak Signal Early Detection

• Productivity increase (Idle, Pause trends, etc…)

• OEE measurement covering all test platforms and operations

11

O+ Solution Works/Results & TrendsEarly Detection (ED)

• Benefit

• Detect anomalies/marginalities as early as possible to

reduce tester stoppages (anticipate test cell controller alarms)

• Achieved performances:

• Improved process control through tightening of bin,

overall yield and site to site deviations by implementing

dynamic rules (O+ calculated) moving from sigma of 3 and

IQR of 5 (initial) to 2 (early detection mode)

• Overall decreasing trend of alarms with tightening of rules implying

process is more under control

• Estimated gains of 1% test cell utilization

12

Plat. APlat. B

Production optimization• Pause Time Reduction

• Review & alignment of Alarms settings

• Identification of product & test equipment

related losses.

• First wafer effect

• Cleaning Frequency DOE

• Index Time DOE

• Identification of index time variance between probers

• DOE based alignment to faster prober

• Investigations showed that reduction in index time induced instability and hence action not retained.

13

ImprovementsTester hours saved

(based on 4 weeks

production)

7111 CRL wafers

corresponding (12” / 8” equivalent, for

TT=45min)

Pause time reduction

target: 2%196 261# / 641#

Cleaning frequency

increase: 80 to 16017 to 149 23# to 199# / 58# to 488#

Index Time Reduction:

5% to 9%

35 to 63(not retained due to induced

instability)

47# to 84# / 115# to 206#

7105 UMC

Index time correction

20

(not retained due to

induced instability)

27# / 65#

Cleaning Time Reduction:

2% to 6%5 to 16 7# to 22# / 18# to 53#

0130 CRL

Cleaning Time correction1.5 2# / 5#

Total for the period 274.5 to 445.5 367# to 595# / 900# to 1460#

Compute for 1 week

basis on the 10 Plat A69 to 113 92# to 149# / 225# to 365#

Half tester saved

Wafers 01 have more

occurrences

Retest reduction• Benefits

• Off-line retest reduction through improved test process integrity

• On-line retest reduction through optimized (intelligent) retest

• Results and Trends

• Several products families retest data analyzed and retest set up modified

• Online retest reduced from 4.86% with a gain of 14.3% to 2.96%

and a gain of 27.5%

14

To measure, To improve: OEE

OEE Definition in ST 16

1. OEE =𝑇𝑜𝑡𝑎𝑙 𝑈𝑝𝑡𝑖𝑚𝑒 (𝑇𝑈)

𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 (𝑇𝑂𝑃)x

x𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔 𝑇𝑖𝑚𝑒 (𝑇𝑃𝑅)

𝑇𝑜𝑡𝑎𝑙 𝑈𝑝𝑡𝑖𝑚𝑒 (𝑇𝑈)x

𝑇ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐺𝑂𝑂𝐷 𝑝𝑖𝑒𝑐𝑒𝑠 𝑎𝑐𝑡𝑢𝑎𝑙𝑙𝑦 𝑡𝑒𝑠𝑡𝑒𝑑 (𝑇𝑃𝑇)

𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔 𝑇𝑖𝑚𝑒 (𝑇𝑃𝑅)=

2. OEE =𝑇𝑜𝑡𝑎𝑙 𝑈𝑝𝑡𝑖𝑚𝑒 (𝑇𝑈)

𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 (𝑇𝑂𝑃)x

𝑇ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐺𝑂𝑂𝐷 𝑝𝑖𝑒𝑐𝑒𝑠 𝐹𝐼𝑅𝑆𝑇 𝑃𝐴𝑆𝑆 𝑎𝑐𝑡𝑢𝑎𝑙𝑙𝑦 𝑡𝑒𝑠𝑡𝑒𝑑 (𝑇𝑃𝑇)

𝑇𝑜𝑡𝑎𝑙 𝑈𝑝𝑡𝑖𝑚𝑒 (𝑇𝑈)=

3. OEE = 𝑇𝑃𝑇

𝑇𝑂𝑃= Availability x Uptime Efficiency x Quality Rate

Note 1: Quality Rate (Yield) is embedded in the formula as TPT includes the GOOD Units only !

Note 2: TPT is the theoretical production time of the good units obtained through the FIRST PASS only (i.e. :

Rework and Retest are excluded)

OEE: Increased Productivity

• Enabling a fact-based Macro detractor measurement in terms of

• Engineering, Unloading, Inefficiency

• At test cell level, to improve OEE:

• When test cell is not testing:

• System is linked to MES and to O+ solution

• Orgnization able to target main source of unefficiency such as:

• Down time

• Lack of operator

• When cell is under testing

• We retrieve in «real-time» data from O+ solution

• Real time targeting source of unefficiency such as:

• Prober issue

• Wafer replacement cycle time

17

Example of OEE analysis 18

OEE increases as its

macro detractors are

quantified and attacked

Test Effectiveness: Quality Firewall

Excursion lifecycle 20

If undetected it

impacts a

consistent part of

the WIP

If Detected at this

stage can become:

Internal

Excursion

It impacts our

customers

Customer excursionAn Excursion starts with

an event (a mistake or a

combination of them)

…not yet called Excursion

…no BIG signal

Prevention: avoid the generation

of the initial small event

How: Proper risk management at

all stages of development and

HVM (FMEA, FMKM, APQP)

Impact minimization:

1. Detect the event

2. stop its proliferation

3. Dispose the impacted material.

It happens AFTER the generation of the initial event

How: SPC, EWS, FT, PAT, SBL / SYL, MRB….

Detection failed

Problem solving methodologies

How: 8D / WHY WHY / SRC to do:

• Containment

• Correction

• Prevention of reoccurrence

How to anticipate detection? 21

If undetected it

impacts a

consistent part of

the WIP

If Detected at this

stage can become:

Internal

Excursion

It impacts our

customers

Customer excursionAn Excursion starts with

an event (a mistake or a

combination of them)

…not yet called Excursion

…no BIG signal

Prevention: avoid the generation

of the initial small event

How: Proper risk management at

all stages of development and

HVM (FMEA, FMKM, APQP)

Impact minimization:

1. Detect the event

2. stop its proliferation

3. Dispose the impacted material.

It happens AFTER the generation of the initial event

How: SPC, EWS, FT, PAT, SBL / SYL, MRB….

Detection failed

Problem solving methodologies

How: 8D / WHY WHY / SRC to do:

• Containment

• Correction

• Prevention of reoccurrence

Quality Firewall Prediction Step 22

LOT TEST START

QUALITY SCORE

QUALITY SCORE

QUALITY SCORE

QUALITY SCORE

Quality score of my lot

Maximum value before test start

PARAMETRIC TEST

EWS

FINAL TEST

Violations to Quality Firewall reduce Quality Score:

Control limits at PT, single point failure, etc.

Violations to Quality Firewall reduce Quality Score:

Control limits at current tests, multiple retests, etc.

Violations to Quality Firewall reduce Quality Score:

Control limits, multiple retests, site to site, etc.

Lot below minimum Quality score

Not released to customer (additional tests or scrap)

• Is a good lot really good ?

• Identify outlier lots via a Quality Score which combines 100+ small signals

based on performance at each test step (Parametric, EWS, FT)

• Pilot running in Q3 on selected product

Pilot

UM10

36 tests

330 tests

127 tests

Quality Firewall steps: summary 23

1. Enhanced Statistical controls, based on more

sophisticated statistical analysis

• SPC breakthrough by implementation of state of

art algorithm

• Overhaul of existing statistical controls (PAT,

SBL, SYL) : PAT algorithm choice, bin

assignment and Bin Limit calculation

• Control limits deployment, from PT to Final Test

2. Quality Index deployment

• Quality index calculation by lot and within lot by

wafer to identify ‘maverick’ material potentially at

risk

Monitors and KPI

• Critical products deployment:

Completed action vs. planned actions

• Critical products performance: Tralica

(ppm after actions) by product

• Effectiveness KPI: Number of

products having 0km failures vs.

number of delivered products

• Efficiency KPI: Number of 0km failures

addressed by project actions vs. total

number 0km failures

PR

EV

EN

TIO

NP

RE

DIC

TIO

N

SPC Breakthrough: Why ?

• Market requests to intercept PPB (parts per billion)

• Need to increase detection capability at every step of the production process

• Statistics methodology applied in manufacturing has continuously improved

but had no breakthrough for several years

• Software and tool capability allows to optimize how to treat the tremendous

amount of data from all plants

• ST set up a network of ST Statisticians (STATS) to drive innovation in

statistics & SPC

24

Excursion example 1 – 700Ku scrapped 25

Limits applied in fab

Limits applied in fab

Correct calculated limit

Correct calculated limit

Event generating the excursion

Missed Out of control!!

Electrical Test: Enhanced Statistical controls

• Enhanced Statistical controls for outlier and abnormal lots detection,

based on sophisticated statistical analysis to cover these areas:

1. New PAT algorithm (O+)

• Previous company disappeared; new features added into new SW

• Includes Dynamic PAT, Geographic PAT, Z-PAT, NNR

2. New algorithm to set SYL / SBL limits (Bootstrap) validated

3. Control limits at electrical test level full deployment

• Perimeter: Parametric test, EWS, FT

• Mandatory for Quality Firewall Prediction step

26

New PAT Algorithm: Geographic

• GPAT, ZPAT, DPAT

• GDBN (Good Die in Bad Neighborhood) – yield

based local nighborhood

• Zonal exclusion – exclude dice and zones

• NNR (Nearest Neighbor Residual) – parametric

level neighborhood

• Cluster detection

27

How to Anticipate Detection ?

• Problem statement: how to detect small signals not detected today ?

• ST already uses several best known methods to detect outlier lots but this is

not enough

• Equipment and process control at wafer fab and assembly plant

• SBL, SYL during EWS, FT

• PAT (geographical, parametric including sophisticated algorithms such as NNR)

• Quality Firewall project wants to identify sophisticated algorithms to detect

small signals not caught today. Two options analyzed:

• Option 1: Univariate combination of PT, EWS, FT parameter

• Option 2: Data science

28

Excursion eradication: next steps

How ST Reduced Excursions 2012-15

• ST launched specific initiative to reduce excursions back in 2012

• Main areas of focus 2012-15 have been:

• Establish clear excursion definition aligned to customer, clear process to manage crisis

• Set up process to manage manufacturing stability

• Set up process for effective baseline defectivity (QIP) with closed feedback loop

• Implement BKM and tools at testing and SPC (Outlier detection, CLM, Quality firewall)

• Review of the process to manage changes (CRB)

• Improve management of incoming material

• Reinforce operational discipline in specific area

• Reinforce engineering bandwidth in specific FE, BE (LEAP)

• Eradicate SRC (Systemic Root Cause)

• Specific task force on technologies / products with high PPB level

30

Excursion trend 2012-15

ALL ST customers Automotive key customer only

Excursion reduction trend 2012-15 continues

2012 2013 2014 2015

NOTE: Excursion definition not fully aligned in 2012 out of automotive

31

Vision & Next challenges 32

How to remove outliers ?

• Traditional approach (Univariate Outliers screening) does not fully

protect the customers from quality issues

• Existing algorithms for univariate outlier filtering have significant impact

on costs (yield loss due to PAT, statistical bin limits, etc.)

33

MULTIVARIATENew approach• SPL (Statistical parametric Limit)

• Data science methods to analyze ALL parameters

at a time (from univariate to multivariate)

• Break the silos (between SPC and electrical tests,

between parametric and electrical test, etc.)

• To rebuild and map overall test data @ wafer level

Data science: Vision

• Big Data is the new wave of structural change in the semiconductor industry

• Big Data is the boost of company digitalization toward “data centric brain on” company

• The whole operation chain “integration” of digital systems with suppliers and customers will

reshape the market and the industry

• Big Data will result into a cultural change in the industry, giving engineering access to key

analytics and predictive information

• Opportunities is about:

• Profit : Improve manufacturing efficiency like yield improvement, preventive maintenance to

predictive maintenance, early failure detection…

• Market Share : Cost savings and better quality to improve competitiveness

• Innovation : Understand quicker customer need by monitoring real time usage of our products.

(relationship to be set up by sales & marketing)

34

From Preventive & Reactive to Predictive quality 35

REACTIVE

quality (8D)

PREVENTIVE

quality (FMEA)

REAL TIME / NEAR TIME monitor

(MES, SPC, STOT, etc.)

PREDICTIVE

Use data science algorithm to predict failures before they happen

Big data journey 36

Traditional enterprise

- Data warehouse with heavy taxonomy, silos approach

- Business intelligence tools (BO)

- Data is a cost not an asset

- Business needs identified not achieved with traditional approach- Vision to use data science to make a breakthrough on business. Data perceived as a potential asset- Build a small core team of data scientists to understand which needs can be fulfilled and define a strategy to achieve the vision.

- Implement the data science strategy: algorithms, architecture, evangelization of the employees. The data science team grows. Data science becomes a key competence in the company

- Data centric company. Integrated, Flexible, Real Time Analysis. Improvements on quality, yield, marketing, overall business management

Which competencies do we need ? 37

Semiconductor

Expertise

Computer science Math and statistics

ICT Statistics for semi

Data

scientists

Virtual factory approach

Manage manufacturing steps in all factories as ONE virtual factory

• Including both internal and subcontracted manufacturing

• Provide feedback thanks to data analysis from manufacturing to R&D

• Adaptive testing

38

Predictive Quality Example

• Experiment done on 5 equipments in one fab

• Development of predictive models (supervised)

• Incremental class-sampling analysis

• Single model vs. equipment-specific model

• Decision Tree Boosting selected among several algorithms

• Algorithm optimized for single equipment

Stepper

alignment

information

Photolithography

production step Outcome

on wafer

Learn predictive models of

good/bad wafers from

photolithography equipment

measurementsWafer-level

analysis

39

Data Science: Target Roadmap

• Step 1: Analytics

• Set up the competency Center

• 5 or more data scientists

• 4 subject matter expertize (Manufacturing, Design, Supply Chain, Quality)

• Competency Center set up in 2016

• Roadmap for infrastructure to be consolidated by Q4 2016

• Step 2: Infrastructure full implementation

• Stage 1 => 2016: Provide dedicated analytics for deeper analysis and modeling

• Stage 2 => 2017: Use Hadoop to be able to grab operational data (structured, unstructured)

• Stage 3 => 2018: Full Hadoop implementation for query-able archive: key for quality!

• Stage 4 => 2019: Full real time processing and analysis on the data ocean

• Stage 5 => 2020: Project completion to ST data-centric company

40

Thank you

41