U.S. patent application number 11/127314 was filed with the patent office on 2006-11-16 for capacity management in a wafer fabrication plant.
This patent application is currently assigned to SYSTEMS ON SILICON MANUFACTURING COMPANY PTE. LTD.. Invention is credited to Chen Chong Chin, Yew Kuan Ho, Hsiang Ju Su.
Application Number | 20060259173 11/127314 |
Document ID | / |
Family ID | 37420200 |
Filed Date | 2006-11-16 |
United States Patent
Application |
20060259173 |
Kind Code |
A1 |
Chin; Chen Chong ; et
al. |
November 16, 2006 |
CAPACITY MANAGEMENT IN A WAFER FABRICATION PLANT
Abstract
In a wafer fabrication plant, the capacity management process 10
begins with identifying an initial capacity plan based on a demand
plan (step 12). For each Product Group in the initial capacity plan
a consumption sensitivity factor is defined (step 14). Next, a
bottleneck capability variable is calculated (step 16). The
capacity boundaries for each of the product groups are next
determined (step 18). Thereafter, in the first of two branches, a
determination of maximum wafer output is then performed for
changing Product Group mixes to determine a maximum (step 20). The
Product Group mix giving maximum wafer output is then determined
for the fabrication plant (step 22). In the second branch, a
determination of maximum profit is performed for changing Product
Group mixes (step 24), then the Product Group mix giving maximum
profit is determined for the fabrication plant (step 26).
Inventors: |
Chin; Chen Chong;
(Singapore, SG) ; Su; Hsiang Ju; (Singapore,
SG) ; Ho; Yew Kuan; (Singapore, SG) |
Correspondence
Address: |
ROTHWELL, FIGG, ERNST & MANBECK, P.C.
1425 K STREET, N.W.
SUITE 800
WASHINGTON
DC
20005
US
|
Assignee: |
SYSTEMS ON SILICON MANUFACTURING
COMPANY PTE. LTD.
Singapore
SG
|
Family ID: |
37420200 |
Appl. No.: |
11/127314 |
Filed: |
May 12, 2005 |
Current U.S.
Class: |
700/99 ;
700/103 |
Current CPC
Class: |
Y02P 90/20 20151101;
G05B 19/41865 20130101; G05B 2219/32263 20130101; G06Q 10/0631
20130101; Y02P 90/02 20151101; G05B 2219/32294 20130101 |
Class at
Publication: |
700/099 ;
700/103 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1-5. (canceled)
6. A computer program product for the management of capacity in a
wafer fabrication plant comprising a computer program stored on a
storage medium, said computer program performing the steps of: (a)
calculating a bottleneck capacity factor for an initial product
group mix of an initial capacity plan, the bottleneck capacity
factor representing available machine hours per a given time period
for said initial capacity plan; (b) calculating maximum capacity
values for respective product groups of the initial product group
mix in the initial capacity plan, each respective maximum capacity
value representing a maximum number of wafers of the respective
product group output by said fabrication plant per said given time
period; (c) algorithmically determining total output values for
respective different product group mixes, including for the initial
product group mix of the initial capacity plan, subject to said
bottleneck capacity factor and said respective maximum capacities
values not being exceeded, each total output value representing a
sum of individual output values of all the product groups in the
respective different product group mixes by said fabrication plant
per said given time period; (d) determining a maximum one of said
total output values; and (e) providing a modified product group
mix, the product group mix being one of said different product
group mixes corresponding to said maximum total output value.
7. A computer program product according to claim 6, wherein said
bottleneck capacity factor is calculated as the sum of weighted
maximum capacities for the respective product groups of the initial
product group mix.
8. A computer program product according to claim 7, wherein each
said weighted maximum capacities is proportional to the sum of
passes of a bottleneck tool for each piece of the respective
product groups and is inversely proportional to production rates of
the respective product groups.
9. A computer program product according to claim 6, wherein, in
step (c), said each total output value comprises a total wafer
output value being the sum of individual wafer output values of all
the product groups in the respective different product group mixes
by said fabrication plant per said given time period.
10. A computer program product according to claim 6, wherein, in
step (c), said each total output value comprises a total profit
value being the sum of individual profit values of all the product
groups in the respective different product group mixes by said
fabrication plant per said given time period.
11. A method of management of capacity in a wafer fabrication plant
comprising a computer program stored on a storage medium, said
computer program performing the steps of: (a) calculating a
bottleneck capacity factor for an initial product group mix of an
initial capacity plan, the bottleneck capacity factor representing
available machine hours per a given time period for said initial
capacity plan; (b) calculating maximum capacity values for
respective product groups of the initial product group plan in the
initial capacity plan, each respective maximum capacity value
representing a maximum number of wafers of the respective product
group output by said fabrication plant per said given time period;
(c) algorithmically determining total output values for respective
different product group mixes, including for the initial product
group mix of the initial capacity plan, subject to said bottleneck
capacity factor and said respective maximum capacity values not
being exceeded, each total output value representing a sum of
individual output values of all the product groups in the
respective different product group mixes by said fabrication plant
per said given time period; (d) determining a maximum one of said
total output values; and (e) utilizing a modified product group
mix, the modified product group mix being one of said different
product group mixes corresponding to said maximum total output
value, in operation of the fabrication plant.
12. A method according to claim 11, wherein said bottleneck
capacity factor is calculated as the sum of weighted maximum
capacities for the respective product groups of the initial product
group mix.
13. A method according to claim 12, wherein each said weighted
maximum capacities is proportional to the sum of passes of a
bottleneck tool for each piece of the respective product groups and
is inversely proportional to production rates of the respective
product groups.
14. A method according to claim 11, wherein, in step (c), said each
total output value comprises a total wafer output value being the
sum of individual wafer output values of all the product groups in
the respective different product group mixes by said fabrication
plant per said given time period.
15. A method according to claim 11, wherein, in step (c), said each
total output value comprises a total profit value being the sum of
individual profit values of all the product groups in the
respective different product group mixes by said fabrication plant
per said given time period.
Description
FIELD OF THE INVENTION
[0001] The invention relates broadly to a method for the management
of capacity in a wafer fabrication plant and to a computer program
product for the management of capacity in a wafer fabrication
plant.
BACKGROUND
[0002] Semiconductor wafer fabrication plants typically produce
thousands of devices per day and may be configurable to fabricate
two, three or more different product groups/types. E.g. commercial
8 inch wafer fabrication plants costs typically US$1.5 billion to
build, representing a significant capital investment for even the
largest enterprises.
[0003] Profitability is of vital importance to the operators and
owners of wafer fabrication plants, and such people endeavour to
improve profitability without relying only on further capital
expense in installed equipment. There thus is a need to optimise
the use of existing installed equipment.
[0004] As mentioned, wafer fabrication plants will produce more
than one semiconductor product. Conventionally, the mix of products
being manufactured at any one time is based on a demand plan and a
derived corresponding initial capacity plan. Such initial capacity
plans are reactive to customer ordering, and associated with a
tooling plan. But conventional initial capacity plans are not
optimised, and thus there is a need to improve upon them, with the
goal of improved profitability or plant output.
SUMMARY
[0005] In accordance with a first aspect of the present invention
there is provided a method for the management of capacity in a
wafer fabrication, the method comprising the steps of (a)
calculating a bottleneck capacity factor for a product group mix of
an initial capacity plan; (b) calculating a respective maximum
capacity for each product group in the capacity plan; (c)
algorithmically determining a respective production value for
different product group mixes, including for the product group mix
of the initial capacity plan, subject to said bottleneck capacity
factor and said respective maximum capacities not being exceeded;
(d) determining a maximum one of said production values; and (e)
determining the product group mix for said maximum production
value.
[0006] Said bottleneck capacity factor may be calculated as the sum
of respective weighted maximum capacities for the individual
product groups.
[0007] Each said product group weighting may be proportional to the
sum of passes of a bottleneck tool of said fabrication plant for
each piece of the respective product group and is inversely
proportional to a production rate of the respective product
group.
[0008] In step (c), said respective production values may be a
measure of total wafer output provided for the respective product
group mixes.
[0009] In step (c), said respective production values may be a
measure of profit provided for the respective product group
mixes.
[0010] In accordance with a second aspect of the present invention
there is provided a computer program product for the management of
capacity in a wafer fabrication plant comprising a computer program
stored on a storage medium, said computer program performing the
steps of (a) calculating a bottleneck capacity factor for a product
group mix of an initial capacity plan; (b) calculating a respective
maximum capacity for each product group in the capacity plan; (c)
algorithmically determining a respective production value for
different product group mixes, including for the product group mix
of the initial capacity plan, subject to said bottleneck capacity
factor and said respective maximum capacities not being exceeded;
(d) determining a maximum one of said production values; and (e)
determining the product group mix for said maximum production
value.
[0011] Said bottleneck capacity factor may be calculated as the sum
of respective weighted maximum capacities for the individual
product groups.
[0012] Each said product group weighting may be proportional to the
sum of passes of a bottleneck tool of said fabrication plant for
each piece of the respective product group and is inversely
proportional to a production rate of the respective product
group.
[0013] In step (c), said respective production values may be a
measure of total wafer output provided for the respective product
group mixes.
[0014] In step (c), said respective production values may be a
measure of profit provided for the respective product group
mixes.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 is a schematic block diagram embodying the
invention.
[0016] FIG. 2 is a tabulation of variables leading to the
bottleneck capacity measure.
[0017] FIG. 3 is a tabulation of variables for various mix
combinations leading to maximum wafer output.
[0018] FIG. 4 is a surface plot of the data of FIG. 3.
[0019] FIG. 5 is a contour plot of the data of FIG. 3.
[0020] FIG. 6 is a tabulation of variables leading to the profit
margin for Product Groups.
[0021] FIG. 7 is a tabulation of variables for various mix
combinations leading to maximum profit.
[0022] FIG. 8 is a surface plot of the data of FIG. 7.
[0023] FIG. 9 is a contour plot of the data of FIG. 7.
[0024] FIG. 10 is a schematic representation of a computer system
suitable for performing the techniques described herein.
DETAILED DESCRIPTION
Overview
[0025] A wafer fabrication plant typically produces semiconductor
devices using a large number and variety of basic fabrication
steps. The steps will depend upon the form (eg. MOS) of device
being fabricated, the nature of the gate (eg. metal or polysilicon)
and the substrate (eg. bulk silicon or silicon-on-sapphire). In
silicon-gate processes a number of discrete sub-processes are
performed. By way of broad example, the steps can include the
definition of active regions, definition of depletion loads,
polysilicon-defusion interconnect, definition of transistors and
polysilicon-defusion contacts, defusion, polysilicon-metal and
defusion-metal interconnects, metallisation and annealing and
passivation. All of these processes and sub-processes require
complex and expensive equipment or tools. It is often the case that
one process step and corresponding tool is used for all product
groups being fabricated.
[0026] FIG. 1 shows a block flow diagram embodying capacity
management in a wafer fabrication plant, according to the present
invention. The capacity management process 10 begins with
identifying an initial capacity plan based on a demand plan (step
12). For each Product Group in the initial capacity plan, a
consumption sensitivity factor is defined (step 14). Next, a
bottleneck capability variable is calculated (step 16). The
capacity boundaries for each of the product groups are next
determined (step 18).
[0027] Thereafter, in the first of two branches, a determination of
maximum wafer output is then performed for changing Product Group
mixes to determine a maximum (step 20). The Product Group mix
giving maximum wafer output is then determined for the fabrication
plant (step 22). In the second branch, a determination of maximum
profit is performed for changing Product Group mixes (step 24),
then the Product Group mix giving maximum profit is determined for
the fabrication plant (step 26).
Specific Example
[0028] Assume X,Y,Z . . . are Product Groups in the Fabrication
plant. Then, the reference fabrication output (OUT.sub.0) is given
by: OUT.sub.0=X.sub.0+Y.sub.0+Z.sub.0+ 1 For the purposes of
illustration, three Product Groups will be assumed, although there
can, of course, be any desired number.
[0029] Referring to FIG. 2, for the three Product Groups X,Y,Z a
known respective maximum capacity (in e.g. pieces/month) is given:
X.sub.max=15,300, Y.sub.max=8,000 and Z.sub.max=5,000. The initial
capacity plan specifies an initial capacity for each Product Group:
X.sub.0=14,000, Y.sub.0=6,800 and Z.sub.0=4,000. The determined
output, OUT.sub.0, thus is 24,800, in accordance with Equation 1.
The initial percentage Product group mix according to the initial
capacity plan of X %: Y %: Z % is equal to 100%: 33%: 16%.
[0030] For each of the Product Groups, the sum of passes
(PASS.sub.x,y,z) for the process using the bottleneck tool,
together with the weighted wafer per hour (WPH.sub.x,y,z), are
given as:
PASS.sub.x,y,z: sum of passes of the process using bottleneck tool
for each piece of product group X, Y, Z
WPH.sub.x,y z: weighted WPH of process passes for each product
group X, Y, Z
[0031] The values of PASS.sub.x,y,z and WPH.sub.x,y,z are given in
FIG. 2.
[0032] A Product Group Consumption Sensitivity Factor for each
Product Group is defined as: a = PASS X WPH X , .times. b = PASS Y
WPH Y , .times. c = PASS Z WPH Z ( 2 ) ##EQU1##
[0033] The values of a, b and c are also given in FIG. 2.
[0034] Therefore the maximum Bottleneck Capability (CAPA.sub.0) in
the example embodiment is calculated as:
CAPA.sub.0=aX.sub.0+bY.sub.0+cZ.sub.0 3
[0035] Therefore, the maximum Bottleneck Capability for the data
shown in FIG. 2, calculated in accordance with Equation 3, gives
the value 5,657 as available machine hours per month in the example
embodiment.
[0036] The Product Groups'Capacity Boundaries X.sub.max, Y.sub.max,
Z.sub.max are defined as:
X.sub.max=Max capacity of X product group due to dedicated
tool(s)
Y.sub.max=Max capacity of Y product group due to dedicated
tool(s)
Z.sub.max=Max capacity of Z product group due to dedicated
tool(s)
Maximum Wafer Output
[0037] The objective is to maximize wafer output in accordance with
Equation 4 for Product Group mix combinations. This determination
is subject to boundary conditions given by Equations 5 and 6:
Maximize OUT.sub.i=X.sub.i+Y.sub.i+Z.sub.i where i: any mix
combination 4 Boundary(1):
CAPA.sub.i=aX.sub.i+bY.sub.i+cZ.sub.i.ltoreq.CAPA.sub.0 6
Boundary(2): X.sub.i.ltoreq.X.sub.max, Y.sub.i.ltoreq.Y.sub.max,
Z.sub.i.ltoreq.Z.sub.max 5
[0038] FIG. 3 shows a series of mix combinations of the Z Product
and the Y Product with reference to the X Product. For each
combination the wafer output is calculated according to Equation 1
and the bottleneck capability is calculated in accordance with
Equation 3. Once these values are determined for all mixes, a
maximum mix combination is determined for maximum wafer output
subject to the bottleneck capability not being exceeded. The data
shown in FIG. 3 is represented as a surface plot in FIG. 4 and as a
contour plot in FIG. 5.
[0039] By mathematical process of interpolation, the maximum wafer
output is given for a percentage Product Group mix of X %: Y %: Z
%=100%: 25.2%: 19.6%. This represents an optimized Product Group
mix, compared with the initial mix from the initial capacity
plan.
[0040] The result of the analysis is that a maximized wafer output
of 25,445 units is achieved by an optimized mixed combination, as
opposed to 24,800 units according to the mix of the initial
capacity plan.
Maximum Profit
[0041] Taking into account the profit maximization aspect, the
profit margins for each Product Group are calculated by the
difference in the selling price and cost, in accordance with
Equations 7, 8 and 9. PF.sub.X=ASP.sub.X-STD COST.sub.X 7
PF.sub.Y=ASP.sub.Y-STD COST.sub.Y 8 PF.sub.z=ASP.sub.Z-STD
COST.sub.Z 9 Again, using the consumption sensitivity factors and
capacity boundaries: Maximize
Profit.sub.i=X.sub.i*PF.sub.X+Y.sub.i*PF.sub.Y+Z.sub.i*PF.sub.z 10
[0042] where i=any mix combination Boundary(1):
CAPA.sub.i=aX.sub.i+bY.sub.i+cZ.sub.i.ltoreq.CAPA.sub.0 11
Boundary(2): X.sub.i.ltoreq.X.sub.max,Y.sub.i.ltoreq.Y.sub.max,
Z.sub.i.ltoreq.Z.sub.max 12
[0043] In the present example, this is shown in FIG. 6 as the
values PF.sub.x=300, PF.sub.y=500 and PF.sub.z=100.
[0044] Maximizing profit is determined algorithmically for mix
combinations of Product Groups, in accordance with Equation 10. The
profit margins act as weightings. The calculation is subject to the
boundary conditions of the bottleneck capacity not exceeding the
initial value (Equation 11), and that the mix components do not
exceed respective maximum values (Equation 12).
[0045] FIG. 7 shows the same tabulation as FIG. 3, but with the
profit calculation, according to Equation 10, performed and given
in the last column. FIG. 8 is a surface plot representation of the
percentage Y product and percentage said product mixes and the
profit value. FIG. 9 is a contour plot of the same data of FIG. 8.
The maximum profit point is calculated by interpolation and gives
maximum profit for the percentage Product Group mixes, X %: Y %: Z
%, of 100%: 34.3%: 1.6%.
[0046] The result of this analysis is that a maximum profit of
approximately $US8.63 million is a achievable by an optimized
Product Group mix as opposed to the US$8 million profit that would
be achieved by the nominal Product Group mix according to the
initial capacity plan.
[0047] It will be appreciated that the results obtained from the
optimization processing in example embodiments of the present
invention may be utilized in a number of ways. For example, where
possible, the optimized Product Group mix may be implemented
instead of the nominal Product Group mix according to the initial
capacity plan. In practice, this may involve the results being
considered during capacity management planning and possible
feedback and interact with the demand plan management. It will
further be appreciated that the results of the optimization
processing in example embodiments may be utilized to facilitate
forecasting in capacity management, and may also provide valuable
feedback in terms of identifying higher and lower profitability
Product Group mixes. This in turn may influence the type of product
groups offered or focused on in the overall management of a wafer
fabrication plant.
Computer Implementation
[0048] FIG. 10 is a schematic representation of a computer system
100 suitable for executing computer software programs. Computer
software programs execute under a suitable operating system
installed on the computer system 100, and may be thought of as a
collection of software instructions for implementing particular
steps.
[0049] The components of the computer system 100 include a computer
120, a keyboard 110 and mouse 115, and a video display 190. The
computer 120 includes a processor 140, a memory 150, input/output
(I/O) interface 160, communications interface 165, a video
interface 145, and a storage device 155. All of these components
are operatively coupled by a system bus 130 to allow particular
components of the computer 120 to communicate with each other via
the system bus 130.
[0050] The processor 140 is a central processing unit (CPU) that
executes the operating system and the computer software program
executing under the operating system. The memory 150 includes
random access memory (RAM) and read-only memory (ROM), and is used
under direction of the processor 140.
[0051] The video interface 145 is connected to video display 190
and provides video signals for display on the video display 190.
User input to operate the computer 120 is provided from the
keyboard 110 and mouse 115. The storage device 155 can include a
disk drive or any other suitable storage medium.
[0052] The computer system 100 can be connected to one or more
other similar computers via a communications interface 165 using a
communication channel 185 to a network, represented as the Internet
180.
[0053] The computer software program may be recorded on a storage
medium, such as the storage device 155. Alternatively, the computer
software can be accessed directly from the Internet 180 by the
computer 120. In either case, a user can interact with the computer
system 100 using the keyboard 110 and mouse 115 to operate the
computer software program executing on the computer 120. During
operation, the software instructions of the computer software
program are loaded to the memory 150 for execution by the processor
140.
[0054] Other configurations or types of computer systems can be
equally well used to execute computer software that assists in
implementing the techniques described herein. In the example
embodiment, the optimization processing was implemented utilizing a
Microsoft.RTM. Excel application program, including the Solver
function in that application program.
[0055] It will be appreciated by a person skilled in the art that
numerous variations and/or modifications may be made to the present
invention as shown in the specific embodiments without departing
from the spirit or scope of the invention as broadly described. The
present embodiments are, therefore, to be considered in all
respects to be illustrative and not restrictive.
* * * * *