Models: runner-ups in

system design

Roelof Hamberg, research fellow

Industry-as-Lab

research

projects

Exploratory

research

Consolidation

& transfer

Industrial problem

statements

New competitive

capabilities

Fundamental insights

& expert know-how

Industrial relevant

research drivers

RE

SE

AR

CH

PR

OD

UC

T

DE

VE

LO

PM

EN

TIndustry

Philips, ASML, FEI,

VI, Thales, Océ

Universities

TU/e, UT, TUD,

RU, UU, RUG, UvT

ESI

Industrial

constraints &

scalability

context

2

Outline

3

models&patterns

verify

explore options

generate

But…

this is theory;

let’s start with

three real examples

ASML machine: “Print the Netherlands in 30 seconds”

minimum feature size 30 nmpositional accuracy 5 nm

300 mm

300 mm wafer

300 km

The Netherlands

300 kmx 1 million

corresponds to 3 cmpositional accuracy 0.5 cm

Source: ASML

4

ASML challenge

5

hard real-time (µs) ↓interactions ↑control loops ↑

position

sensors

position

measurement

position

controlactuators

power

amplifiers

position

set-point

wafer stagemotion controlled

predictable timing?

4000 tasks250 sensors/actuators14000 dependencies

Complex electronics platform Many design files & formats

ASML case – approach: performance modeling

platform

model

executable

system model

system

specification

& realization

performance

measurements

performance

prediction

gauging

validation

optimization &

simplification

application

modelless impact

of slack

P1 P2 P3

X X

P1 P2 P3

X X

validation

6

modelingengineering

ASML – performance model results

7

platform

model

executable

system

model

embedded

system

construction

(design)

application

model

mapping

many interacting subsystems4000 tasks250 sensors/actuators14000 dependencies

Manymulti-disciplinary design files & formats

averageworst

latency

ASML benefits

• Product improvement

– 16 different improvement options identified

– over 50% performance gain

– determination of platform limits

• Improvement roadmap

– Cost/risk analysis

– Decision trees

5 µs 4 µs 2 µs 2 µs 7 µs 5 µs 2 µs

3 µs5 µs 1 µs 4 µs 1 µs

1 � ¬1 ☺

11 � 11 �

4 ☺

3 � ¬3 ☺

4 ☺

0

08

11

16.2

12.4

19

3

6.6 3

8.4 6.3

4 ☺

4.8

2 � 9 � 2 �

0

¬2 ☺

short term roadmap

mid term roadmap

8

Océ challenge

9

PDF/PostScript paper

variable timings ↑job dependent scheduling ↑

sufficient throughput?

cost price ↓

sizing?memory?

scheduling?configuration?

building prototype

is expensive: effort

and lead time �

many decisions to

take, behavior

difficult to predict �

don’t think too long, but build!

Analysis

module

Modeling module

(specification)

Kernel module

(intermediate representation)

Diagnostics

module

Search

module

Océ case – tuned towards ease-of-use & exploration

10

trade-offs between system aspects

measurements (logs)compared to

model predictions

Océ benefits

• Early validation beyond spreadsheets

– low investment (< 1 day)

– more insight in dynamics (variability, resource sharing over time, flow control)

– applied in real cases, impact on design decisions

• Multiple properties validated

– 1 specification → multiple models– consistent models

• Design decisions

– guided search through options

11

Vanderlande systems: interwoven in society

12

Imagine:Distribution centre failure

of supermarket at Friday

Consider:New shops, new articles,

Christmas time, …

Vanderlande challenge

13

Goederen Ontvangst en pallet magazijn

OmpakkenDepalletiseren

Opslag lagentrays & ontladen Tray beladen

Rolcontainer beladenHDS picking

Rolcontainer buffer

Verzendgebied

I SS PickingHDS picking

4

2000

Aisl

e 01

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42 (27) dc68 (14) sc

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4000 (263 6)

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1000 (63 6)

12 00 (1027)

180 0 (1540)

700 (154 )

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1 20 (107)180 (162)

200 (108)

1 80 (162)Transport naar droog

item picking

3 000 (1010)

(1010 )

500 (440 )

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160 (15 7)

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186 (157)

12 0 (64)

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464

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(190)

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240 (184) l agen/h

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140 (81) 14 0 (81) 140 (81)

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(32)

2500 250 0

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van droog item picking

140 (81)

79 (53)

600 (28)

188

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10 m(1 080)

1 0 x

400 (269) ge nest

90 (80)

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15

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1500 (1027)

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2 40 (158) l agen/ h

240 (1 59)

90

300 0 (978)

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450 0 (4108)

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1 500 (292)

Miniload “st aart”

1500

3 shuttles

3 shuttles

300 (257)

40 00 (3515)

customer data dependencies ↑unscheduled down time ↓variability in business processes ↑

sufficient throughput?

availability?first time right laborious layout

can you make a

good design in

short time?

Vanderlande – model approach & results

14

scripts

statistics foro throughputo work-in-processo lead times

visualization of schematic system

Vanderlande benefits

• Early validation of design parameters

– system sizing, buffer sizes, control rules

– visualization to verify behavior

– full layout not needed

• Customer system configuration

– towards toolbox supportcustomer data → filter → concept simulation

– standardized processes and design steps

15

so...

what have we seen?

some reflections

16

Early validation of system aspects

17

verify

• visualization of candidate systems

• non-technical stakeholders

• increased insight →recipe for growth

Excel++: Executable system models

18

• overcomestatic limitations

• average → optimistic

• worst → pessimistic

• spreadsheet

real product

• documentation

dynamicmodels

Think before you act

19

chose from many options

• evidence-baseddesign choicesbefore prototyping

• increased insightin parameter effects

• model structure

• smart search &optimization

Design process efficiency

20

generation

• generate system from modelthe model is your system

• need for stable architecture

• standardized process

Models: runner-ups in

system design

21

Embedded Systems InstituteEindhovenwww.esi.nl

Roelof.Hamberg@esi.nl