COST OF OWNERSHIP (COO) FOR OPTOELECTRONIC MANUFACTURING EQUIPMENT

COST OF OWNERSHIP (COO) FOR OPTOELECTRONIC MANUFACTURING EQUIPMENT Sid Ragona, Ph.D. Burleigh Instruments, Inc. 7647 Main Street Fishers, Victor NY 14564-8909 SRagona@burleigh.com ABSTRACT To increase the cost effectiveness of the component assembly process, it is first necessary to be able to determine the cost of the existing process and provide a mathematical model that can be used to predict the outcome of changing the process or the effectiveness of purchasing new equipment. The mathematical model presented here is based on the SEMI E35 standard for calculating the cost of ownership (COO) of manufacturing equipment. In this paper we present the basic input parameters required for the calculation and demonstrate the power of the cost modeling approach by comparing two hypothetical workstation that are intended for pigtailing PLC devices. In addition we show how the cost of the parts being assembled affect the overall cost effectiveness of the equipment being used. Keywords: Cost of ownership, COO, yield, component manufacturing, SEMI E35 INTRODUCTION In the last two to three years there have been a plethora of articles comparing the fledgling optoelectronic market with that of the semiconductor IC market. A common theme contained within these articles is that the semiconductor market matured because of its ability to automate and establish common working standards. While these assertions are essentially true they are also superficial and fail to recognize that part of the maturation process of the semiconductor industry was its ability to develop a common cost-modeling standard, known as cost of ownership (COO). The adoption of a common COO standard allowed IC and wafer manufactures to objectively scrutinize their manufacturing operations and have a common framework for process development decisions as well as equipment purchase decisions. In addition, adopting the common costmodeling standard, SEMI E35 [1] also allowed equipment supplier s access to the same decision making tools as the equipment users. Consequently, equipment vendors were no longer shooting in the dark to find the common elements of the decision-making matrix of their customers, and were able to make better-informed decisions on future product offerings. Thus, the early adoption of a common costmodeling scheme for both equipment users and equipment suppliers assisted the rapid deployment of a hugely successful and highly efficient automated manufacturing industry. Conversely in the optoelectronic components industry there was until recently no need for cost modeling algorithms since scalability did not require sophisticated equipment; rather it simply required hiring more people for manual assembly with a total disregard for yield. However with the recent down turn in the industry and the rapid deployment of IC-type technology for waveguide manufacturing, the need for a more sophisticated cost modeling method such as E35 is now well overdue. Because of the lack of such a standard in the optoelectronics industry, many companies are developing their own cost modeling strategies independent of each other and without prior knowledge of the work that all ready exists and that has stood the test of time. Implementation of a common cost modeling strategy will result in the creation of strategic economic goals that both the equipment users and equipment vendors can use for the benefit of the entire industry. In this article we explain the basic concepts of COO cost modeling and illustrate its usefulness as a decision making tool for purchasing manufacturing equipment. COO METHODOLOGY What Does COO Model? The COO value that is calculated represents the cost of the process step per good part produced. The COO value that is calculated indicates the cost added to that part from the process. The SEMI E35 standard defines COO as the full cost of embedding, operating and decommissioning, in a factory and laboratory environment a system needed to accommodate a required volume. Thus in order to determine the COO of a piece of equipment one must divide the full cost of the equipment and its operation by the total number of good parts produced over the commissioned lifetime of the equipment. The Basics of the COO Calculation There is a lot written about COO modeling [2] as it applies to the semiconductor industry. Many of the papers written go well beyond the COO of a process step and delve into associated Cost of Measurement (COM) theory [3]. The basics of COO are quite simple and can be summed up in the equation: COO = Total costs Total number of good parts

A breakdown of the major variables that contribute to the numerator and denominator of the above equation are outlined below: Numerator Input Parameters The total cost of owning the equipment consists of the sum of four major parameters Where: Total cost = F$ + L$ + R$ + Y$ F$ represents the initial fixed costs involved in purchasing the equipment. Purchase price, cost of installation and initial training, system qualification and any movement of the equipment in the factory. L$ represents the fully burdened labor cost. It is common to classify labor costs with those of the reoccurring costs R$. However, it has been found beneficial to treat them as a separate parameter, so that the impact of changing labor rate and alterations in the number of shifts that can be worked may be more closely scrutinized. R$ represents the sum of all the reoccurring costs (excluding labor, L$) associated with the equipment over its lifetime and includes such things as; consumables, maintenance, utilities, floor rent and specialized support personnel. Consumables are defined as parts of the instrument that are worn out and need replacing in less than a year. Maintenance includes the cost of service contracts and/or visits, spare parts and replacement parts and any additional training that is distinct from the initial training. Specialized support personnel is the cost of using supervisors and engineering support services. Floor rent is the equipment s share of the factory overhead. Floor rent becomes increasingly more expensive per square foot, the higher the class of the clean room. For clean room environments a major factor affecting the floor rent is the operational footprint of the equipment. The operational footprint of the equipment is distinct from the actual physical area of the instrument and includes all required space around the equipment that needs to be available for service and operator personnel. For example, a workstation may have a contact footprint of around 8-9 sq. ft, yet the operational footprint probably is on the order 50 sq. ft. With class 100 clean rooms running about $400 per sq. ft. per year, the floor rent may be as high as $20K per year. Y$ represents the yield cost associated with the equipment s mishandling of the devices. The cost of lost yield due to the equipment is the product of the probability that the device will be damaged by the equipment in question multiplied by the cost of the device at that processing step. As one might expect, for any given probability that the device could be damaged, the cost of lost yield increases as one travels from the front end of a process to the back end of the process. The magnitude of the yield cost is the product of the number of defective assemblies (N) that are directly attributable to the workstation multiplied by the values of the parts at that stage of assembly (P$). Thus: Y$ = N x P$ Denominator Input Parameters The total number of parts produced by the equipment is dependent on four major variables summarized in the equation below: Where: Total number of parts produced over the life of the equipment = L x T x Y x U L represents the entire lifetime of the equipment (3-5 years for semiconductor equipment) T represents the throughput rate (parts per hour) Y represents composite yield. The number of good units produced. Y essentially represents a scaling factor, i.e. some value slightly less than one. If metrology equipment is used for inspection, then a number of defective units will be identified causing a decrease in the yield. Furthermore, to protect against shipping bad product, occasionally good product may be measured and classified as bad. While this is known as an alpha error (good product classified as bad) of the metrology tool, it may be mistakenly attributed to the process step. Unfortunately it is the yield parameter that generally has the biggest impact on the COO value, but is also the most difficult parameter to determine. U represents equipment utilization. This value can be represented as some portion of the lifetime of the equipment. Utilization acts as a scaling factor reducing the throughput of the system. There are five major factors that cause utilization to be less than 100%. These factors can best be described in the formula below. All values are in hours per week. Where: U = 1 - SM + USM + A + S + Q H SM represents scheduled maintenance. USM represents unscheduled maintenance. Unscheduled maintenance requires information on Mean Time between Failure (MTBF) and Mean Time between Assists (MTBA). A represents assist time. Assist times are not failures but are essentially interruptions that require a reset and do not require spare parts; often this is as simple as rebooting the computer. S represents standby time, i.e., actual time the system is available for use and not being used. Interestingly enough in scenarios where the proceeding process step is

bottlenecked, higher throughput rates for the existing step can lead to longer standby times, thus providing very little real gain in the COO. Q represents qualification of the equipment. (recalibration procedures) H represents total number of scheduled production hours per week. Thus, the basic formula for COO can be written: COO = F$ + L$ + R$ + Y$ L x T x Y x U Effective Implementation of COO In order for COO to be used as an effective tool, information must be shared between the equipment supplier and the equipment user. Some of the input parameters are the responsibility of the equipment supplier and some of the equipment user, and some are mutually dependent and need to be agreed upon. Some input parameters that are totally equipment supplier dependent are such things as initial purchase price, throughput and the cost of maintenance contracts and MTBF and MTBA data. Whereas, other input parameters are totally equipment user dependent, such as information on labor rates, number of shifts and days worked, floor rent, the cost of the components at that stage of assembly and actual or intended usage of the equipment. Some of the input parameters may have no clear demarcation and will require the cooperation of both parties. One example of an input parameter that needs the input from both equipment supplier and equipment supplier would be the time and frequency of the re-calibration (Qualification, Q) of the equipment for determining the Utilization (U). For example, the time between calibrations will be dependent on the tolerances specified by the equipment user, whereas the time taken for the recalibration under these conditions needs to be specified by the equipment supplier. Without the cooperation between both parties the calculation of the COO value is open to misuse and misrepresentation. EQUIPMENT PURCHASE DECISIONS One of the most appropriate applications of COO is for aiding in the decision making process for selecting and purchasing process equipment [2]. In many cases the purchasing decision by the equipment user is based on trying to assess the inherent values of many different features offered by competing equipment suppliers. In many cases the selection process is first narrowed down to two or three tools that are perceived to have the ability to do the job. From this point on it is common for the decision making process to be centered on obtaining the lowest priced equipment and assurances that service and help (if ever needed) will be readily available, local and speak the same language. Unfortunately, this type of decision making is based more on emotions and less on objectivity. Using the COO cost-modeling criteria as an aid to the decision making process allows for more objectivity in determining the appropriate course of action. To illustrate the usefulness of COO calculations we compare the economic merits of two hypothetical alignment and bonding workstations for planar lightwave circuits, (PLCs) applications. The workstations are designed for aligning and bonding an input fiber array (IFA) and an output fiber array (OFA) to either end of PLC chip, depicted in Figure 1. A typical example of an assembly station workstation is shown in Figure 2. For the purposes of discussion the two workstations being compared are hypothetical and do not represent any one particular equipment supplier. However, the input parameters for the COO cost modeling are based on real world numbers recently collected from equipment suppliers and PLC manufacturers. Applications Cost of ownership methodology can be applied to a wide variety of situations, including but not limited to, equipment purchase, equipment comparison, equipment benchmarking, competitive analysis, project prioritization and changes to the companies business model, i.e., moving production overseas to reduce labor costs. One of the most useful applications of COO modeling is when it is used to compare equipment to aid in the purchasing decision. Below we illustrate the utility of COO by applying it to an equipment comparison to be used as the basis for an equipment purchase decision. Figure 1 schematically shows the alignment of input and out fiber arrays to a planar lightwave circuit.

Number of shifts/day 3 3 Labor rate per year per shift $40,000 $40,000 Utilities and floor rent per year $20,000 $20,000 Consumables per shift per year (adhesive, $6,000 $6,000 syringes, etc) Diced and polished 40- channel PLC device $1500 $1500 Input fiber array $150 $150 Output fiber array $850 $850 COO $86.6 $64.2 Figure 2. Typical assembly workstation designed for high yield assembly operations. Workstation A costs $170,000 with an hourly throughput of five units per hour, whereas workstation B is $80,000 more expensive at $250,000 with a lower throughput rate of four units per hour. A quick analysis of the price and throughput rate indicates that workstation A appears to have the upper hand, especially since it is considerably less expensive than B and the throughout rate is 25% higher than B. Furthermore the annual service costs for workstation A are $9,600 less per year than the expensive, lower throughput workstation B. The yields for both instruments appear almost the same at 97% and 98% for workstations A and B respectively. Given this information one may be tempted to chose workstation A as the best value for money. It would in fact be quite difficult in many cases to justify to upper management, the decision to buy workstation B, at the higher initial price, lower throughput and more expensive service contract. However, the decision to buy one instrument or the other requires a more detailed analysis of the intended use for the workstation. By including the data on the cost of the parts being assembled, the number of shifts being worked and the intended lifetime of the equipment one can effectively use COO cost modeling analysis to determine the most economical purchase as shown in Table 1. Workstation Workstation Parameter A B Initial purchase price $170,000 $250, 000 Yearly service contract based on 12.5% of $20,400 $30,000 original purchase price Throughput per hour 5 4 Yield 97% 98% Utilization 70% 70% Lifetime of equipment 3 years 3 years Number of workdays 6 6 per week Table 1 lists the input parameters used to determine the final COO value for two workstations with different purchase prices, throughputs and yields. Despite the lower initial purchase price and higher throughput of workstation A, workstation B has the lower COO value due to its 1% higher yield. By taking into account the usage of the workstation, the COO calculation shows that it is in fact workstation B that is the most economical. Workstation B produces parts that are $24.4 less expensive than workstation A. Over a period of three years the savings amount to approximately $2.75m, which is more than 10x greater than the initial purchase price. Thus, the original purchase price difference of $80,000 between the two workstations is hardly significant in this scenario. The large difference between these two COO values can be attributed to the 1% difference in yield between the two systems. The effect of this 1% yield difference is more apparent when one analyzes the individual contributions that make up the final COO values as shown in Figure 3 and Table 2. COO ($) 90 80 70 60 50 40 30 20 10 0 Workstation A Reoccuring Labor Purchase price Scrap Workstation B Figure 3 graphically displays contributing cost factors associated with determining the COO values of workstations A and B. In both cases the Y$ (scrap) is dominant. The combined costs of labor, recurring costs and initial purchase prices represent only a small fraction of the total cost of owning and operating the equipment over three years.

Assembly machine A COO = $ 86.6 B COO = $ 64.2 Cost input Cost contribution to COO value COO value Y$ $ 77.3 89.3 % F$ $ 2.2 2.6 % L$ $ 4.7 5.5 % R$ $ 2.3 2.7 % Y$ $ 51 79.4 % F$ $ 4 6.3 % L$ $ 5.8 9 % R$ $ 3.3 5.2 % Table 2 shows individual contributions of Y$, F$, L$ and R$ to the COO value for both workstations. For expensive components such as large channel count PLCs the cost of scrap is the predominant cost factor in determining the COO value. The combined contributions of the labor, recurring costs and purchase price represent only a fraction (~11-20%) of the total costs incurred. An analysis of Table 2 shows that in both cases, it is the yield costs that predominate in contributing toward the COO value. For every part produced on workstation A, $77.3 of the cost per assembly is derived from the scrap costs compared to $51 from workstation B. Whereas, the contribution of the initial purchase price to the COO values is $2.2 and $4 for workstations A and B respectively. The contributing cost of labor on the final COO value is $ 4.7 and $5.8 for workstations A and B respectively. Thus, under these conditions only marginal cost savings can be attained by reducing labor costs. Only after the yield costs have been dramatically cut would labor rates start to represent the dominant contribution to the COO value. An analysis of the yield cost in terms of the number of defective units produced is depicted in Table 3. An analysis of Table 3 shows that over a three-year period workstation A produces approximately one additional defective component per day more than workstation B. Consequently workstation A is $2500 per day more expensive to operate than workstation B. System Defective parts Cumulative cost in 3 years over 3 years Workstation A 2359 $ 5.89 M Workstation B 1258 $ 3.14 M Difference 1101 $ 2.75 M calculation. Consequently moving fabrication to a lower wage per capita region makes sense only when issues with the high cost of yield have been solved. 40 35 30 25 COO ($) 20 15 10 5 0 99 99.2 99.4 99.6 99.8 99.9 Yield (%) Reoccuring Labor Purchase price Scrap Figure 4 shows the sensitivity of COO as a function of yield for workstation B. At yields above 99.6%, the labor cost becomes the most significant cost contributor to COO COO as a Function of the Cost of Parts Being Assembled It can be argued that the high cost of the parts being assembled is largely responsible for workstation B having a lower cost of ownership than workstation A. While this is true to some extent, an analysis of the COO value as a function of the cost of the parts being assembled for both workstations is presented in Figure 5. Figure 5 shows that lowering the cost of the parts being assembled lowers the COO value for both workstations. However, even when the costs of the parts are reduced from $2500 to $500, workstation B still has the lower COO value. In fact, for parity between the two workstations the cost of the parts being assembled has to be reduced to $380 in the scenario presented. When the cost of the parts being assembled is below $380, workstation A has the lower COO value. For example, if the parts being assembled cost $100, then under the current scenario described above, workstation A would have the lowest COO value at $12.2 per assembly compared to $15.4 per assembly for workstation B. Under these conditions workstation A still has the higher scrap costs. However, this is offset by the added cost of workstation B and higher cost of labor per good part produced. Table 3. Shows the number of defective parts produced by both workstations over the life of the equipment. Workstation A produces additional 1101 defective components over a three-year period at a cost of $2500 each. Thus, over a three-year period this additional scrap cost represents a $2.7 M loss. The sensitivity of the COO value to a small change yield is illustrated in Figure 4. The data is for workstation B. Only after yields have risen to 99.6% do the yield costs become lower than the labor costs which now predominate the COO

COO ($) 100 90 80 70 60 50 40 30 20 10 0 Workstation A Workstation B 2500 2000 1500 1000 500 100 Cost of parts being assembled ($) REFERENCES [1] SEMI E35, Cost of Ownership for Semiconductor Equipment Manufacturing Metrics, Semiconductor Equipment and Materials International, San Jose, CA, 2000. [2] Carnes, R. and Su, M., Long Term Cost of Ownership: Beyond Purchase Price, Proc. 1991 IEEE/SEMI International Semiconductor Manufacturing Science Symposium, pp 39-43. [3] Dance, D., Estimating the Cost of Ownership for Test and Metrology, Proceedings of the Second Annual Manufacturing Test Conference. 1992, pp 17-22. Figure 5 shows the COO values for workstations A and B as a function of the cost of the parts being assembled. Reducing the cost of the parts being assembled reduces the corresponding contributions of yield cost for both workstations. CONCLUSIONS The basic equation presented here for calculating the COO originally was developed for wafer fabrication tools and has become a common term between equipment suppliers and equipment users in the semiconductor industry. In the arena of optoelectronic component manufacturing it is virtually unknown. This is not surprising since the optoelectronic component manufacturers were predominately concerned with innovation as opposed to components that could be manufactured in an automated or semi-automated process. Consequently the only means available for scaling up the process was to hire more people. Under these kinds of conditions cost modeling was quite simple, the cost per parts did not need to be recalculated, since more of the same would be produced by simply repeating the number of manual work-cells available. These conditions could only survive so long as astronomical prices would be paid for the components being manufactured. After all, with yields being in the order of 10-30%, it is quite likely that close to 95-99% of the manufacturing cost was derived from the scrap costs. With a down turn in the market for optical components and the increasing demand for high channel count PLCs and hybrid chips, there now is a more conscious effort to develop manufacturing processes that are cost effective. Equipment suppliers can develop more appropriate tools for the equipment users when there is a common dialogue and understanding on the cost model being used. ACKNOWLEDGEMENTS The author would like to acknowledge many helpful discussions with Bill Gornall, on the subject of COO and for his critical reading of the manuscript.