intelligence augmentation in high-tech manufacturing · industry 1.0 mechanical production powered...

Intelligence Augmentation in High-Tech Manufacturing

Ahmer Srivastava, Director of AnalyticsHDD Wafer Operations

October 2017

Big Data Analytics & Smart Manufacturing: Only if it were that easy…

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©2017 Western Digital Corporation or its affiliates. All rights reserved.

©2017 Western Digital Corporation or its affiliates. All rights reserved. Confidential.

• In a strong strategic position to lead global evolution of broad-based and changing storage industry

• Broad storage portfolio, including HDDs, SSDs, embedded and removable flash memory, and storage-related systems

• Vertically integrated business model to maximize operational efficiency

• Consistent profitable performance, strong free cash flow

~72BTotal Addressable

Market (TAM)*

13,500+Total Issued Patents,

Worldwide

70000+Employees Worldwide

*


+

A Global Leader in Storage Technology

©2017 Western Digital Corporation or its affiliates. All rights reserved. Confidential.©2017 Western Digital Corporation or its affiliates. All rights reserved.

A Global Leader in Storage Technology

11/14/2017 6©2017 Western Digital Corporation or its affiliates. All rights reserved.

Innovation is Enabling Rapid Rate of Data Generation

11/14/2017 7

Exponential Growth in Data Generated


11/14/2017 8

Delivering the Possibilities of Data

©2017 Western Digital Corporation or affiliates. All rights reserved.©2017 Western Digital Corporation or its affiliates. All rights reserved.

Anatomy of a Hard Disk Drive (HDD)

Head Stack Assembly

Disk

Spindle Motor

Flex Circuit & Pre-amp Device

Base Machined Casting

Voice Coil Magnet

PCBA & Electronics

Air Filter

Load & Unload Casting

Internally Manufactured

©2017 Western Digital Corporation or affiliates. All rights reserved.

Anatomy of a Hard Disk Drive (HDD)Disk Clamp

Top Disk

Bottom Disk

Spacer

TopVCM

Head Stack Assembly

BottomVCM

Ramp

Top Cover

Motor BaseAssembly

Latch

Recirc Filter

Read-Write Head on Head Gimbal Assembly

Casting • Forging • Machining •

Stamping • Molding • SMT •

Semiconductor • Substrates •

Epoxies & special adhesives •

Rare earths • High precision assembly

Internally Manufactured


Hard Disk Drive: A Very Complex Piece of HardwareDEALING WITH COMPLEXITY


Imagine one hair sliced into 17000 pieces. Each piece is ~6nm wide.

Assuming, head flies over the disk at a height of <10 nm.

No asperities allowed above ~6nm glide height - one asperity rejects the surface!

The relative fly height of the head and flatness of the disk media surface is equivalent to:

Flying from SF to NY at a height of <1m without contact

reference: Eric Champion, Western Digital, circa 2014

Reader Sensor

Air Bearing Surface (ABS) Writer Main Pole

Wa

fer

Pro

ce

ss

De

vic

e S

tac

k B

uil

dMagnetic Head Fabrication. 3-Dimensional, Complex Nanoscale Fabrication

Nanoscale 3D fabrication process requiring tight tolerance controls

Long processing time, delayed feedback

Material layers down to 3-4 atoms thick, requiring high precision

Functional geometries <30nm

Final device structure must withstand severe mechanical stress

z

x

y

Perspective: Individual Magnetic Heads on a Dime

WAFER MAGNETIC HEAD (DEVICE)


6 Decades of Innovations – Phenomenal Scaling!HOW DO WE SURVIVE?

IBM RAMAC(first HDD)

CAPACITY

12 TB

FORMAT

8 disks 3.5”

AREAL DENSITY

864 Gbit/sqin

DATA RATE

12 Gb/s

POWER

~10 W

COST/MB

<$0.000032

Ultrastar HE12

(latest HDD)

2016

2.4M xincrease

>99.9%smaller

432M xdensity

80K xfaster

99.6%less power

1956

CAPACITY

5 MB

FORMAT

50 disks 24”

AREAL DENSITY

2 Kbit/sqin

DATA RATE

150 Kb/s

POWER

2374 W

COST/MB

$10,0001x 108

cost improvement

1 source: anandtech2 source: website©2017 Western Digital Corporation or affiliates. All rights reserved.

http://www.anandtech.com/show/10888/western-digital-announces-ultrastar-he12-12-tb-and-14-tb-hdds

http://www.statisticbrain.com/average-cost-of-hard-drive-storage/

Operational Realities with Increasing Product Complexity in Manufacturing WHY DO WE NEED TO INVEST?

OPERATIONS REALITIES• Long total cycle times• Large number of permutations• Global manufacturing footprint• Leading edge technology• Increasing product complexity• High unit volumes

BUSINESS DRIVERS• Cost reductions

• Quality improvements

• Improving asset utilization

• Improve labor efficiency

• Highly competitive environment

Courtesy of Dave Rauch, SVP Western Digital


Aligning Smart Factory Initiatives to Stakeholder KPIsWHAT ARE WE TRYING TO ACHIEVE?

BusinessDrivers

B O T T O M L I N E

Less variation

Improved asset utilization

Improved efficiency

No deviations• No misprocessing

• Improved quality and reliability

• Improved customer experience

Tighter parametric distributions• Higher yields

• Lower DPPM

• Matched equipment

Improved time to problem resolution• Faster cycles of learning

• Faster new product ramps to maturity

Lower cost• Faster cycle times

• Higher asset efficiency (OEE)

• Lower LOH

• Test time reduction

• Higher engineer / management productivity


Enablers & ChallengesDEFINING OUR JOURNEY

ENABLERS• Computing power at low cost• Cheap, available sensor technology• Increasing Bandwidth• Significant collection of data• Most of data is structured

CHALLENGES• Production, process and product

data are fragmented

• Sampling plans are not cohesive

• Costs money and disrupts production

• Unclear payoffs

• Lack of appropriate skill set

• Financial and mind-share risk

Courtesy of Dave Rauch, SVP Western Digital


Industry 4.0 – Next wave in ManufacturingOVERVIEW OF INDUSTRIAL REVOLUTIONS

Adapted from Christoph Roserhttp://www.allaboutlean.com(Wikipedia)

Industry 1.0 Mechanical production powered by steam and water

Industry 2.0 Mass production assembly lines, division of labor, powered by electricity

Industry 3.0 Automated production using advances in electronics and information technology

Industry 4.0 Cyber Physical Systems; Intelligent production incorporated with IOT, cloud and big data; physical and software components deeply intertwined

18th Century 19th Century 20th Century Today


http://www.allaboutlean.com/

Smart Manufacturing (SM) enables all information about the manufacturing process to be available when it is needed, where it is needed, and in the form it is needed across the entire manufacturing value-chain to power smart decisions. Smart Manufacturing Leadership Coalition SMLC / CESMII

Artificial intelligence (AI) is intelligence exhibited by machines. Study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal.

Intelligence Augmentation (IA), on the other hand, is the idea of a computer system that supplements and supports human thinking, analysis, and planning, leaving the intentionality of a human actor at the heart of the human-computer interaction.

Key Emerging Themes related to Smart Factory: General Definitions


https://smartmanufacturingcoalition.org/

https://www.cesmii.org/

https://en.wikipedia.org/wiki/Intelligent_agents

SOURCE: https://iot-analytics.com/industrial-internet-disrupt-smart-factory/


interconnectedness

https://iot-analytics.com/industrial-internet-disrupt-smart-factory/

ANALYTICS

LEARN & DOEXECUTION & PROCESS CONTROL MES, SPC, APC, FAULT DETECTION, PREDICTIVE MAINTENANCE (PdM), REAL-TIME DISPATCH …

OPERATIONS OPTIMIZATION OPERATIONAL EFFICIENCY ON FACTORY FLOOR, CYCLE TIME, CAPACITY PLANNING & SCM, EQUIPMENT UTILIZATION, PLANT SIMULATION …

ADVANCED ANALYTICS PERFORMANCE & QUALITY ANALYTICS, ANOMALY DETECTION, PATTERN RECOGNITION, PRODUCT YIELD OPTIMIZATION, FIELD RELIABILITY …

FACTORY

THINGSFACTORY

FLOOR

AUTOMATION

CONNECTHOST INTERFACE, DAQ/IP, GATEWAY, LOW POWER DEVICES, MESSAGING

STORAGE & COMPUTE

COLLECT

DATA STORE, DATA LAKE, BIG DATA LOCAL | DV | CLOUD | HYBRID

BATCH

STREAM

EVENT PROCESSING

UDFs

Based on Ishikawa & Lean Six Sigma

6 M’s of Manufacturing

Clear strategy for capturing all relevant data in the automation layer is key

Generalized Framework for Factory Operations and Product Analytics


• Next-generation Advanced Process Control … overview of APC

• Virtual Metrology & Virtual Fabrication (digital 3D reconstruction of device for design & process optimization)

• Application of machine learning and deep learning for intelligent pattern recognition & process optimization

• Predictive Analytics for Yield Optimization & Intelligent Testing

• Capacity Modeling, Real-Time Inventory Management & Smart Dispatch

• “Digital twin” for equipment fingerprinting, fleet chamber matching, predictive maintenance (PdM), and plant simulation

• Digital Assistant with AI-based Recommendation Engines

Opportunities for Intelligence Augmentation in a Smart Factory

*”Digital twins” refer to computerized companions of physical assets


Based on Ishikawa & Lean Six Sigma

6 M’s of Manufacturing

Advanced Process Control: An Overview

• APC is an extension of Process Control which fundamentally is about reducing variability• The use of automation technologies + equipment interfaces + applied statistics

• A vehicle for putting into action the insights learned with data science

“In control theory, Advanced Process Control (APC) refers to a broad range of techniques and technologies implemented within industrial process control systems.”

RUN-TO-RUN CONTROL FAULT DETECTION FAULT CLASSIFICATION

KEY COMPONENTS


Feedforward Run-to-Runcontroller

Run-to-Runcontroller

Feedback Feedback

Advanced Process ControlFUNCTIONAL DESIGN

28

Shop Floor

Metrology Process ToolProcess ToolMetrology

Processflow

Metrology

Fault Detection databasefor offline analysis

Fault Detectionmodels

Fault Detectionmodels

Stopprocess

ACTIONS

MESTool Maintenance

MESProduct Hold

EmailNotification

Stop Eq.Process

Stopprocess

Courtesy of Norm Armour, Western Digital

Fault Detection(Tool)

Run-to-Run control(Product)


Case Study: Real-time Fault Detection in Ion MillingSCRAP & REWORK REDUCTION THROUGH FAULT DETECTION MODELS


Ar+

Ar+

Ar+

Ar+

Ar+

Ar+

Ar+

Ar+

Ar+

Ar gas

PBN

Beam grid (+)

Suppressor grid (-)

Material removed

wafer

Ion mill process chamber (simplified)

Redep

Ground

Courtesy of Yeak-Chong Wong et al., Western Digital

Enabled Scrap & Rework Reduction

Real time data analysis:maximum value of each step is saved as analysis data.

Suppressor Current(Hi Power)

Multi-step recipe

Analysis for 1 month of data from Ion Mill Process Module

Suppressor Current

FDC model detects limit for fault condition to trigger actions:Stop Tool, Notify DMT, Put Tool-Down, and Hold Wafer in MES system

Current back to normal after high voltage is applied to grid burning off redep.

PROBLEM: No Fault Detection Tool aborts at single set limit Manual recovery of abort wafer

results in rework or scrap Risk of under-etch wafer escape

SOLUTION: With Detection Implementation Customized FD based on different power levels Stop tool (gracefully) No manual recovery ; avoid wafer rework or scrap Put wafer on-hold automatically

Case Study: Enabling Virtual Metrology leveraging TIBCO Spotfire Platform

• Virtual Metrology (VM) is a promising solution to enhance APC with real-time feedback and lower cost of metrology in factories

• TIBCO Spotfire Platform provides comprehensive data preparation, advanced analytics and visualization capabilities

• Virtual Metrology model built using machine learning approach with TIBCO® Enterprise Runtime for R (TERR) within SpotfirePlatform

• Exploratory data visualization to evaluate prediction performance, model robustness and enable model diagnostics, as well as allowing users to

– Setup proactive alert on model performance

– Export result to relational database

– Integrate with APC system for feedforward or feedback control

Leveraging Spotfire platform for development of Virtual Metrology models to enhance Advanced Process Control. A scalable solution that can be generalized for multiple process modules in factories.

Courtesy of Yeak-Chong Wong, Western Digital


Case Study: Intelligent Pattern Recognition

COMMON FRAMEWORK FOR REPRESENTING KNOWLEDGE

DATA AUTOMATION

DATA VISUALIZATIONENG. PRODUCTIVITY

DIAGNOSTIC

MACHINE LEARNING FOR CLASSIFICATION

PREDICTIVE

EMBEDDED CLOSED-LOOP ANALYTICS (EDGE)

PRESCRIPTIVE

Machine-intelligent ecosystem for continuous detection, classification, and predictive quality management of unit of production

COLLECT VISUALIZE MODEL OPERATIONALIZE


Factory data collection systems unification/simplification and accelerated failure analysisOpportunities for deep-learning approaches for real-time alert & closed-loop recommendation system

Case Study: Intelligent Pattern RecognitionIMPROVING ENGINEERING PRODUCTIVITY WITH AUTOMATED DIAGNOSTIC TOOL


TTL Reduction from Weeks to Minutes

Example Defect Class Types

FEATURE 1 FEATURE 2 FEATURE 3 FEATURE 4 FEATURE 5

AFTER

Engineering Productivity Improvement for Wafer Pattern Identification

BEFORE

LOG

SC

ALE

(TT

L)

AFTER

VIRTUAL FACTORYPHYSICAL FACTORY

1010101110001010010100101010

execute model, analyze, optimize

• SimulateDesign, Process & Planning – Accelerate Learning Cycles

• OptimizeOperation and maintenance of physical assets, systems and manufacturing processes

• Anticipate, Predict & Prescribe“What-If” scenarios to anticipateunexpected events (e.g., tool down event, expediting ship plans, new machine installation, unplanned maintenance) and to indicate required actions (e.g., condition-based maintenance).

A Digital Twin is a computerized companions of physical assets, processes and systems


With ambient intelligence in the “smart” factory, devices would work in concert to support managers and engineering in carrying out their daily activities and tasks in an easy, natural way using information and intelligence from a network of connected devices and systems.

• embedded: many networked devices integrated into the environment context aware: devices can recognize you and your situational context

• personalized: they can be tailored to your needs

• adaptive: they can change in response to you• anticipatory: they can anticipate your desires

without conscious mediation.

Reference: Zelkha et al. 1998; Aarts, Harwig & Schuurmans 2001

Intelligence is not an Individual Trait


“Good Morning, Michael. Would you like your briefing now?”

“Good Morning, Spotty. How am I tracking to the Pack Plan for the week?”

“That’s unfortunate. What is causing the shortfall?”

“Any recommendations on recovery”

Digital Assistant for Factory Management

Factory Assistant

PREVIOUS

I was able to identify 2 mitigation paths to help recover shortfall by 98%.

Factory inventory and equipment analysis identified the following key contributors

Yield on Velo Product 3% lower vs Plan

Tool-05 Excursion in Segment 5: -4.3d delay

High UDT-QUALWAIT for Tool-03: -0.5d delay

NEXT


“You have a few principal bottlenecks in Factory 7 currently impacting Velo product, resulting in a

5% shortfall versus planned pack-out. This means 30-32 unit shipment shortage or ~4d

delay in meeting complete customer demand.”

71% of manufacturers say IoT will have a

notable impact on their business over the

next five years….

SOURCE: Forbes Article “Making Internet of Things (IoT) Pay In Manufacturing” (2016), http://bit.ly/2yIP2Ye

44% of manufacturers say that their biggest obstacle in

leveraging the IoT is their company’s limited

knowledge of how the IoT can improve operations

and products.

The journey to the next frontier…

58% of manufacturers say that improving

product quality is the most important

objective they are pursuing by

incorporating smart devices or

embedding intelligence.


The Prepared Mind: Embrace AmbiguityOpportunity, not a deterrent

ADOPTED FROM: The Process of Design Squiggle by Damien Newman

INNOVATION

“We may not know what that answer is, but we know that we have to give ourselves permission to explore.”

Embrace Ambiguity

Patrice Martin, co-lead and creative director of IDEO.org

Industrial Data Science applications and IIOT in smart factory context are still evolving; it can sometimes feel like working in high uncertainty space.


Building Mindshare ... Going beyond algorithms and technology alone

IDEAS PEOPLE TECHNOLOGYBUILD.MEASURE.LEARN WISDOM OF CROWDS. POLYGLOT ARCHITECTURE.

LEAN FORWARD.

“The Lean Startup” by Eric Ries

“Open Innovation: The New Imperative for Creating and Profiting from Technology”

by Henry ChesboroughGOOD READS

“Precision: Principles, Practices and Solutions for the Internet of Things”

by Timothy Chou



VIDEO

In Summary

We, together as pioneers in digital manufacturing, are at the eve of a new industrial revolution.

We are embarking on a era where confluence of low-cost, scalable computing power, new memory technologies, innovations in sensor

technology, communication protocols and machine-learning algorithms is making our ability to execute on connected factories — and the knowledge

required to run modern “intelligent” factories — a reality.


Thank You!

InsightsMachine Learning

PrecisePredictions

Real-TimeResults

Protection

Automation

Anywhere

Anytime

Analytics

ContextAware

Delivering the possibilities of data

intelligence augmentation in high-tech manufacturing · industry 1.0 mechanical production powered...

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