practical optimization flow using a new system design

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Transactions of The Japan Institute of Electronics Packaging Vol. 8, No. 1, 2015

1. IntroductionIn recent years, improving the speed and performance

of CMOS devices which serve as the basic component in

current semiconductor devices has been realized through

hyper-miniaturization technologies. The miniaturization

technologies have extended beyond the 1x nm node. How-

ever, to further increase speed and performance, the sig-

nal delay in global wiring used for connection of individual

IPs within a chip will be a crucial issue. As a means of sat-

isfying various requirements such as shortening the wir-

ing length, wafer-level three-dimensional integrated cir-

cuits (3DICs) have been proposed.[1] On the other hand,

due to the inclusion of many different types of elements,

product systems using 2D or 3D ICs are large scale and

complex which, in turn, makes system design very diffi-

cult. So far, we have constructed the system design meth-

odology for 3DICs, and proposed a practical method for

the design of large scale and complex systems.

In practice, design optimization is complicated and time-

consuming. To resolve this issue, the System Design Sys-

tem Integration-Cubic (SDSI-Cubic) has been proposed

(Fig. 1).[2] This methodology is applicable for the entire

product system. It efficiently and automatically optimizes

the system by combining various systems engineering

methods based on a defined system profile. However, the

previous design example using the SDSI-Cubic itself was

in early development and it had not been applied to an

practical product design.[3]

In this paper, we apply the SDSI-Cubic to optimize the

design of a radiation measurement system. Thus far, we

have developed technologies for different elements or

components of the radiation measurement system and

made a prototype.[4] However, the prototype does not sat-

isfy all requirements due to priority being placed on sensor

design rather than the entire system, and because the

design was based on human experience and intuition.

The radiation measurement system is an embedded sys-

tem, which requires a small, light weight form factor and

high cost-performance. For dealing with the March 2011

Fukushima nuclear disaster in Japan, residents in

Fukushima disaster area are in immediate need of an eas-

ily available measurement system to measure radiation

and radioactivity. This is the background of the system we

consider in this paper. To satisfy all these design require-

ments, it is necessary to resolve conflicting requirements

within the system and create an optimal design. In addition

to the sensor design, the implementation of various func-

tionalities such as waveform processing and calculation is

designed by first properly assigning it to hardware (Ana-

log Circuits and FPGA) or software for CPU. Moreover, we

[Technical Paper]

Practical Optimization Flow using a New System Design

MethodologyKen Kawamura*, Hidenori Murata*, Yoshiharu Iwata*, Ryohei Satoh**, and Kazuya Okamoto**

*Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

**Osaka University Office for University-Industry Collaboration, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

(Received July 31, 2015; accepted November 19, 2015)

Abstract

An efficient design flow for a radiation measurement system using the SDSI-Cubic (System Design System Integration-

Cubic) is demonstrated. The system itself is embedded and, therefore, requires a small-form factor and high cost-perfor-

mance amongst other things. In addition to sensor design, the implementation of various functionalities such as waveform

processing and calculation is designed by proper assignment/implementation to hardware (analog circuits and FPGA) or

software for the CPU. Moreover, the entire system optimized while accounting for the conflicting requirements of mul-

tiple subsystem designs. By optimizing the system configuration, a high cost-performance portable radiation dose and

radioactivity measurement system is developed.

Keywords: IC Design, System Design, SDSI-Cubic, SysML, Measurement System

Copyright © The Japan Institute of Electronics Packaging

119

Kawamura et al.: Practical Optimization Flow using a New System Design Methodology (2/9)

optimize the entire system considering various system

structure proposals and conflicting design issues.

2. Outline of SDSI-CubicThe SDSI-Cubic method is a system design framework

for the design optimization of large-scale and complex sys-

tem. As shown in Fig. 1, this framework performs auto-

matic optimization using an algorithm which classifies the

systems engineering technology into 6 faces.[5] These

faces correspond to each stage of the system design flow.

Face-1 is the “Input.” It is where input of product system

information and a design intention is performed. Face-2 is

the description and analysis of product information. It is

where system profile definition is performed. Face-3 is the

execution and processing of product information. It is

where the relation between each design problem of a prod-

uct system is formulated, and it changes into the state

which can be evaluated. Face-4 is the description and anal-

ysis of process information. It is where efficient design

process is constructed. Face-5 is execution and processing

of process information. It is where the optimal design solu-

tion is derived. Face-6 is the “output.” It is where the out-

put of a result is performed. This method optimizes the

system design automatically based on the information of

the system profile definition (Face-2). Therefore, the sys-

tem profile definition is very important.

Next, we describe the Face-2 system profile definition

method. The purpose of Face 2 is to process the product

system on a computer by defining and describing paramet-

ric models, and to extract design tasks to perform execu-

tion and processing in Faces-3 and -4.

We use the Systems Modeling Language (SysML) to

create a system profile definition. SysML is a modeling

language developed to support specification, analysis,

design, confirmation, and verification of the whole system

which includes hardware, software, data, procedure, and

equipment. In addition, SysML provides a semantic and

diagrammatic base to represent models for requirements,

behavior, structure, and integration of system reflecting an

engineering result. The diagram of SysML is shown in Fig.

2. A system is described using 9 diagrams ({a}-{i} in Fig. 2)

each belonging to one of 3 different types, i.e., require-

ment, behavior, or structure.[6] In addition, Fig. 2 also

shows an optimization diagram ({j}) and allocation dia-

gram ({k}) which were added to enable optimization using

the SDSI-Cubic.

The SDSI-Cubic system profile definition uses 4 dia-

grams of SysML to describe the system model to be opti-

Fig. 1 SDSI-Cubic Framework.

Fig. 2 The broad view of SysML.

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mized (Fig. 3). Four diagrams are a block diagram, an

internal block diagram, a parametric diagram, and newly

added optimization diagram. By using these diagrams,

information about the optimization problem can be

described. In a system profile, information of the system

structure is described in the block diagram (Fig. 3 (a)),

information of the attributes are described in the internal

block diagram (Fig. 3 (b)), information of the constraints

between the attributes are described in the parametric dia-

gram (Fig. 3 (c)). Moreover, Fig. 3 (d) shows the objective

function and the design parameters of optimization are

expressed in the optimization diagram. In addition, an allo-

cation diagram is prepared to describe and evaluate the

choice of system structures such as selection of hardware

or software.[7]

3. System Profile Definition of Radiation Measure-ment System

In this research, we design a radiation measurement

system using a scintillation counter based on information

obtained from the preceding section. The scintillation

counter uses a scintillator crystal for the sensor. Moreover,

we represent the system using the structures as shown in

Fig. 4. The system consists of a sensor, a processing cir-

cuit, a display, a Pb radiation shield, a sample container, a

case and so forth, with various functionalities, such as

gamma rays acquisition, signal processing, calculation,

and result output. Design proposal 1 focuses a balance of

sensitivity, detection limit, and price. Design proposal 2

considers detecting radiation from sample more efficiently

by using 2 sensors. Design proposal 3 focuses on detecting

radiation from sample more efficiently by enclosing the

sensor with the sample.

Next, we define the radiation measurement system

requirements as well as consider design conflicts. Resi-Fig. 3 SysML diagrams for SDSI-Cubic.

(a) Block diagram.

(b) Internal block diagram.

(c) Parametric diagram.

(d) Optimization diagram.

Fig. 4 Design proposal of the radiation measurement system.

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dents in the Fukushima disaster area require a high cost-

performance system which can check the safety of radia-

tion easily. In particular, we require high sensitivity (1,300

cpm/(μSv/h) or more), high resolution (10% or less), low

detection limit (50 Bq/kg or less), light weight (10 kg or

less), small size (5,000 cm3 or less), and low price (about

1/3 price of existing system). For example, the electrical

circuit requirement is high speed for high sensitivity, and

low noise for high resolution and low detection limit. How-

ever, it conflicts with the requirement of low price since a

high-performance circuit is expensive. Therefore, we need

an optimum design which resolves these conflicts.

In addition to the sensor design, an important aspect of

this design is the assignment of γ -ray signal acquisition,

processing and calculation functionalities to hardware

(analog circuit, FPGA) or software (CPU). We use an allo-

cation diagram to represent the case when there are multi-

ple approach options to realize functionality (Fig. 5). We

define the program size from the processing algorithm

and we formulate choice, cost, functionality and others for

each component. By putting attributes (parameters) in

internal blocks, we represent constraints using parametric

diagrams.

We must answer the following questions regarding how

to implement/assign functionalities:

1) Should the pulse height analysis functionality

(which detects peak voltage from the conversion

voltage of a gamma ray signal) be implemented

using a FPGA or CPU (Fig. 5 (a))?

2) Should the functionality of the spectrum creation

functionality which changes and integrates a pulse

height to an energy value be implemented using a

FPGA or CPU (Fig. 5 (b))?

3) Should the calculations used for radioactivity and

radiation dose measurement functionality be imple-

mented in CPU, notebook PC or tablet PC (Fig. 5

(c))?

4) Should the user interface functionality, e.g., input

and result output, be implemented in a notebook

PC, tablet PC, or a dedicated board including CPU,

button, and display (Fig. 5 (d))?

In addition, we note our system structure will change

dramatically according to our hardware selection. There-

fore, we classify our parametric model into two types of

information which we represent using a parametric dia-

gram (Fig. 6). The first type is the shape-independent

information while the second consists of all other informa-

tion. The shape-independent information consists of all

parameters and constraints which are determined by exis-

tence of an entity. An example of a parametric model of the

radiation measurement system is shown in Fig. 7. The

design proposal choices are described using an allocation

diagram (Fig. 7 (a)). The constraints which are indepen-

dent of the design proposals are described in a parametric

diagram of the radiation measurement system (Fig. 7 (b)).

The constraints which depend on the design proposals are

described in a parametric diagram of each design proposal

(Fig. 7 (c), (d), and (e)). By isolating the shape-indepen-

dent information we make it easy to change a shape pro-

posal and modify the model.

In this design, we define an evaluation value function

shown in (1) which we optimize by maximizing its value.

Moreover, we define the following design parameters, i.e.,

design proposal choices, functionality assignment choices,

Fig. 5 The structure choices of each functionality.

(a) The pulse height analysis functionality. (b) The spectrum creation functionality.

(c) The radioactivity and radiation dose measurement functionality.

(d) The user interface functionality.

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Fig. 6 Example of parametric model proposed in this paper.

Fig. 7 Parametric model of the radiation measurement system.

(a) The structure choices of the radiation measurement system.

(b) Part of the parametric diagram of the radiation measurement system.

(c) Part of the parametric diagram of the design proposal-1.

(d) Part of the parametric diagram of the design proposal-2.

(e) Part of the parametric diagram of the design proposal-3.

123


sensor crystal size, Pb shield thickness, and capacity of

sample container. The comparison of the evaluation value

(SDSI-C) for each design parameter is shown in Table 1.

The design proposal choices are related to sensitivity,

detection limit, weight, volume, and price. The functional-

ity assignment choices are related to price. The sensor

crystal size is related to sensitivity, resolution, detection

limit, and price. The Pb shield thickness is related to detec-

tion limit, weight, and volume. The Sample container

capacity is related to detection limit, weight, and volume.

We represent these design parameters in an optimization

diagram.

SDSI-C = S /( R · L · P · W · V ) (1)

Here, SDSI-C: Evaluation value, S: Sensitivity, R: Resolu-

tion, L: Detection limit, P: Price, W: Weight, and V: Volume.

We define the system structure, the attribute (parame-

ter) of each block, and the formulated constraints of the

relation between each attribute. We then create a system

profile by representing the system structure, the attribute

(parameter) of each block, and the formulated constraints

using the block diagram, internal block diagram, and para-

metric diagram, respectively.

4. Results and DiscussionWe use the Face-2 system profile definition discussed in

section III, and optimize the radiation measurement sys-

tem using the SDSI-Cubic. 26 design tasks shown in Fig. 8

(design Task1-26) are extracted in Face-2, i.e., design task

1: Input of the initial values; design tasks 2–10: Data acqui-

sition from database of each part (the amplifier circuit, the

photodetector, the calculation part, the position sensor, the

spectrum creation part, the user interface part, the com-

munication part, the peak value detector, and the power

supply part); design tasks 11–19: Calculating cost of each

part (the user interface part, the position sensor, the spec-

trum creation part, the peak value detector, the calculation

part, the communication part, the power supply part, the

amplifier circuit, and the photodetector); design task 20:

Calculating the gamma rays emitted by the sample; design

task 21: Calculating weight of the Pb shield and shielding

effect; design task 22: Calculating the gamma rays emitted

by a standard radiation source; design task 23: Calculating

sensitivity and detection efficiency; design task 24: Calcu-

lating detection limit; design task 25: Calculating total cost

(price), total volume and total weight; design task 26: Cal-

culating resolution. We add design tasks of design parame-

ters (design Task00-010) to change the design parameters

in optimization. The generated task group consists of 37

design tasks (design Task1-26 and design Task00-010). We

use the DSM (Design Structure Matrix[8]) to construct a

design procedure for this task group using a minimal

amount of rework (Fig. 8). In Fig. 8, 6 optimization prob-

lems are defined and each line expresses a design task.

OPT1 is an optimization problem of minimizing cost of the

user interface. The design parameter is the user interface

choices (notebook PC, tablet PC, or a dedicated board).


calculation part. The design parameter is the hardware

choice needed to perform the calculation (CPU, notebook

PC, or tablet PC). OPT3 is an optimization problem of min-

imizing cost of the peak value detector. The design param-

eter is the choice of peak value detector (FPGA or CPU).


spectrum creation part. The design parameter is the

choice of spectrum creation functionality hardware (FPGA

Table 1 Comparison of the evaluation value (SDSI-C) for each design parameter.

Evaluation value Objective function Design parameter

No.SDSI-C Sensitivity Resolution Detection

limit Weight Volume Price Design proposal

Pulse height

analysis

Spectrum creation Calculation User

interface

Sensor crystal

size

Pb shield thickness

Sample container capacity

(×10-11) (×104)cpm % Bq/kg kg cm3 (104)yen cm3 mm cm3

1 1.31 2.5 9 52 10 4,500 16 1

CPU CPU Tablet PC Tablet PC 25 10 4502 1.60↑ 4.3 9 47 11 5,500 20 2

3 0.26↓ 2.5 9 38 20 12,000 17 3

1 1.31 2.5 9 52 10 4,500 16

1

CPU CPU Tablet PC Tablet PC

25 10 4504 1.30↓ 2.5 9 52 10 4,500 16.1 FPGA FPGA Tablet PC Tablet PC

5 1.27↓ 2.5 9 52 10 4,500 16.4 CPU CPU CPU Tablet PC

1 1.31 2.5 9 52 10 4,500 16

1 CPU CPU Tablet PC Tablet PC

25 10 450

6 0.37↓ 0.9 9.5 64 10 4,500 14 9 10 450

7 1.54↑ 2.5 9 64 8 4,200 16 25 5 450

8 1.54↑ 2.5 9 78 8 3,500 16 25 10 230

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or CPU). OPT5 is an optimization problem of maximizing

sensitivity, shielding effect and detection efficiency, and

minimizing Pb shield weight and each part cost. The

design parameters are the sensor crystal size, Pb shield

thickness, sample container capacity, and the design pro-

posal choices (design proposal 1, 2, or 3). OPT6 is an opti-

mization problem of minimizing detection limit. The

design parameter is the measurement time. In this study,

the measurement time is 30 minutes. An optimization

result of the radiation measurement system is derived by

performing in order optimization (OPT1-4), calculation

(design Task10/2/22/3/5/8), optimization (OPT5), calcu-

lation (design Task11-19), optimization (OPT6), and calcu-

lation (design Task25/26).

The optimization results are shown in Fig. 9. The graph

is expressed with the axis of price, sensitivity, and weight

and colored with SDSI-C value. The groups of results are

formed by the design proposal choices and other design

parameters. Design proposal 1 has the group of highest

Fig. 8 Result of task DSM partitioning.

Fig. 9 Graph of the optimization results.

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SDSI-C values. Consequently, we obtained the following

optimization results. The design proposal is Design Pro-

posal 1. The pulse height analysis and spectrum creation

functionalities are implemented in the CPU. The radioac-

tivity concentration and dose computation are imple-

mented in a Tablet PC. The user interface functionality is

implemented using a Tablet PC. Sensor crystal size = 50

cm3, Pb shield thickness = 10 mm. sample container capac-

ity is 330 cm3. Using this optimized solution, we were able

to realize a radiation and radioactivity measurement sys-

tem which is both portable and has high cost-performance

(Fig. 10). The optimized solution satisfies the design

requirements, i.e., sensitivity is >10,000 cpm, resolution

about 9%, detection limit 50 Bq/kg, weight 10 kg, volume

4,000 cm3, and price about 1/3 price of the existing sys-

tem. Compared with the prototype, resolution is reduced

from 12% to 9% but price is further reduced from 1/2 to

1/3 of the existing system.

Our optimization shows that in order to reduce the total

cost, the measurement functionality which requires high

speed processing and a control of interrupt should be

implemented using an embedded board, while the calcula-

tion processing functionality should use the PC.

In this design, we considered various design structure

proposals as well as optimizing assignment of functionality

to hardware/software. However, we were unable to deal

with optimizing the physical layout of the combined

mechanical and electrical systems. Moreover, we were

unable to define the associated problems which need to be

simulated. Currently, mechanical and electrical systems

are independently designed using specialized CAD/CAE

tool for each field (mechanical system, control system,

electric system, and software). Therefore, to optimize both

systems together, we must consider cooperation between

SDSI-Cubic and each CAD/CAE tools.

5. ConclusionsIn this study, we optimized the design of a radiation

measurement system required by residents in Fukushima

using the SDSI-Cubic method.

1. The conflicting requirements (high sensitivity, high

resolution, low detection limit, light weight, small

size, and low price) of the radiation measurement

system were considered.

2. The optimization problem including the optimal

assignment of various functionalities to hardware

(analog circuit, FPGA) or software (CPU) was

defined.

3. We proposed a method of representing a parametric

model which differs in structure of the ideal design.

We divide the design into shared (common) models

and models that exist for each shape.

4. Using SDSI-Cubic, the system design was done

automatically. Based on a defined system profile,

the SDSI-Cubic extracted design tasks, constructed

efficient design procedures, constructed evaluation

methods as well as performed optimization.

5. A high cost-performance radiation and radioactivity

measurement system was realized which satisfy the

requirements of residents in Fukushima.

Future work is to research extend SDSI-Cubic by coor-

dinating the system design method with CAD/CAE tools

of different fields (mechanical system, control system,

electric system, and software).

AcknowlegementsIt is gratefully acknowledged that this work was sup-

ported by the JST-SENTAN project and the Japan Society

for the Promotion of Science Committee 177. Thanks also

go to Mr. Takeshi Sakamoto (GLOBALASSIST Co., Ltd.),

Dr. Paul Aoyagi and Shin Nihon Denko Co., Ltd.

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Fig. 10 Photograph of the radiation measurement system, and measurement example of the standard Cesium (134Cs & 137Cs) contaminated soil.

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Ken Kawamura received the B.E. and M.E. degrees in engineering from Osaka Univer-sity, Japan, in 2012 and 2014, respectively. He is currently doctoral course student in Graduate School of Engineering, Osaka Uni-versity, Japan. He is researching the system design methods.

Hidenori Murata received his Doctor of Engineering in 2015 from Osaka University, Japan. From 2014, he has been working as a project researcher at Osaka University. He is currently working on energy system design.

Yoshiharu Iwata received the B.E., M.E., and Ph.D. degrees from Osaka University, Osaka, Japan, in 1990, 1992, and 1994, respectively. He was a Research Associate with the Department of Production Engi-neering, Faculty of Engineering, Osaka Uni-versity. Since 2002, he had been a Research

Associate with the Collaborative Research Center for Advanced Science and Technology, Osaka University. The center’s name has since been changed to the Center for Advanced Science and Innovation. Since 2007, he had been an Associate Professor of this center. Since 2011, he has been an Associate Professor of the Department of Manufacturing Science, Osaka University. His research focuses on interconnection and packaging technology in electronics and system design integration in electronics systems.

Ryohei Sato received the M.E. and Ph.D. degree in engineering from Hokkaido Uni-versity, in 1973 and 1988, respectively. He joined Hitachi Ltd., in 1973. He engaged in the research and development of packaging technology of mainframes computer and Plasm display business. He then became a

Professor in Osaka University in 2001. He engaged in the research and education of the system design and nano-electron-ics. He retired Osaka University in 2013. Now he is Emeritus Pro-fessor and Specially Appointed Professor in Osaka University and is a part-time teacher in Kyoto Institute of Technology.

Kazuya Okamoto received the Diploma from the Harvard Business School, USA, and the Ph.D. degree in electronic engineer-ing from The University of Tokyo, Japan. He joined Nikon Corporation in 1982, and designed/fabricated various kinds of CMOS devices for Nikon’s commercial products. In

2004 he and his group developed a novel electron-beam optics system with IBM for next-generation lithography. Since 2005, he has been a Visiting Professor with Osaka University, Japan. His current research interests include 3D integration and the system design methodology for future semiconductor devices. He is a Mission Executive Fellow in the Japan Institute of Electronics Packaging.

practical optimization flow using a new system design

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