Project Management Institute

Project complexity in the semiconductor industry

a case study approach

Marian Bosch-Rekveldt, PhD, MSc, and

Herman Mooi, PhD, MSc

Abstract

Project complexity can have a large influence on project execution and project success. In this research, a framework describing project complexity that was developed in the process industry was applied to the semiconductor industry. This framework, called the TOE framework, describes project complexity in these three dimensions: technical, organizational, and external complexities. By performing case studies on a number of projects that are carried out by a semiconductor company, we investigated where complexity comes from in these projects and if it is sufficiently described by the framework. The outcome of the research is that the framework describes complexity that is found in semiconductor projects reasonably well, although some modifications could be made, such as adding extra complexities to the framework. The next steps after this research project are looking at the practical application of the framework for use in the front-end phase of projects and expanding the research to other companies, to see whether the findings of this research project are generally applicable to the semiconductor industry.

Target audience: researchers, project managers, decision-makers

Keywords: project complexity; semiconductor industry; case studies

Introduction

Understanding where complexity comes from in a project is crucial when coping with the complexities that one encounters. Because complexity can have an influence on a project's success (Bosch-Rekveldt, Hermanides, Mooi, Bakker, & Verbraeck, 2010), giving sufficient attention to project complexity is important for a company carrying out projects. The main goal of the research project is to understand where complexity comes from in projects in the semiconductor industry and how an existing framework that describes project complexity could be applied to this industry. This paper describes the research that was done in a single semiconductor company on project complexity in the projects that this company executes. The research was done by performing 16 case studies on a selection of projects done within the company.

This article is structured as follows: first, the article presents the literature concerning project complexity and the semiconductor industry; second, the research gap that is addressed in this research project is elaborated upon; third, the methods that were used in the research project are described; fourth, the results of the empirical research are presented; fifth, the conclusions that are drawn from this research project are shown; and, finally, the findings of the research project are discussed, and suggestions are given for further research and for the practical implementation of the research findings.

Project Complexity

In the context of projects and project management, a number of authors have reviewed the literature on complexity(Baccarini, 1996; Remington, Zolin, & Turner, 2009). Baccarini (1996) recognizes different sources of project complexity, namely organizational complexity and technical complexity. In each form of complexity, complexities due to differentiation (meaning that complexity comes from the many parts of a system) and due to interdependency (meaning that different parts of a system have a certain degree of connection) can be identified. Complexities related to the organization have to do with the organizational structure of a project (i.e., the way the project is organized). Complexities related to technical aspects are, as the name implies, related to the actual contents of the project. Another view on complexity in project management is given by Remington et al.(2009). The authors of this paper identify four dimensions of complexity: structural, technical, directional, and temporal sources of complexity. Each of these dimensions has a certain severity in a project, and together they determine the complexity of the project. In the context of the development of complex technical products, a definition was introduced by (Hobday, 1998): Complex Products and Systems (CoPS). Examples of these complex products and systems are technical systems that are developed uniquely (i.e., not in mass production), such as bridges and semiconductor fabrication equipment. Because CoPS often consist of multiple subsystems that need to work together, the architecture of the system plays a large role (Henderson & Clark, 1990). Shenhar and Dvir (1996) and Shenhar (2001) give a typology of engineering projects in two dimensions: system scope (the extent of the system under development; i.e., the place in the hierarchy going from an assembly to an array) and amount of technological uncertainty (from low-tech to super high-tech). This typology is also used by Hobday, Davies, and Prencipe (2005) to extend that framework to the CoPS concept.

Williams (2002) provides a description of complex project management from a systems engineering point-of-view. The use of system dynamics allows the modeler to implement certain characteristics of complex projects into a project model, which can aid in planning the project or in risk management. Cooke-Davies, Cicmil, Crawford, and Richardson (2007) go back to the fact that project management emerged from a Cartesian type of reductionism, but note that there are certain characteristics of complex projects that make this reductionist approach problematic.

There are several (complementary) descriptions of project complexity. Bosch-Rekveldt, Jongkind, Mooi, Bakker, and Verbraeck (2010) give an overview of different definitions of project complexity. In the mentioned article, a third dimension of complexity is added to the complexity framework of Baccarini, namely, complexities that are caused by the environment. These three aspects of complexity (technical, organizational, and environmental) together form the TOE framework (where the acronym stands for the sources of complexity). The TOE framework is described in more detail in the next chapter, where the context of the research problem is investigated further.

∘ The Semiconductor Industry

The semiconductor industry is a relatively young industry, because active semiconductor devices were discovered relatively recently: the first semiconductor transistor was developed in 1947 at Bell Labs (Jenkins, 2005). Since then, developments have happened so rapidly, that in the modern world, life without semiconductors is almost unimaginable. For an example of the history of the evolution of microprocessors and the markets for these components, see Tredennick (1995).

Semiconductors are — as the name implies — materials that conduct electrical current moderately, but are not completely insulating. By injecting low concentrations of other atomic species (so-called dopants) into certain parts of the semiconductor, the local conductivity of the semiconductor device can be adjusted. In this way, engineers have the possibility to design devices that can perform a multitude of tasks. By integrating several active and passive components in a single device, the integrated circuit (IC) was developed. The best-known semiconductor material is silicon. The word “silicon” is often used as a synonym for semiconductor devices or related technology.

The large-scale production of semiconductor devices became profitable in the 1960s and this allowed for the creation of the semiconductor industry (Walsh, Boylan, McDermott, & Paulson, 2005). Since then, semiconductor devices have exponentially increased in complexity. In 1965, Gordon E. Moore even put this into a law-like form. In this ‘law,’ Moore stated that the number of components in an integrated semiconductor device double approximately every two years (Moore, 1965). This prediction has held true until this day.

However, not only is the amount of transistors per chip important. For some applications, it is paramount that chips do not fail under any circumstances (for example, the electronics that control the operation of automobiles or medical applications). Other chips need to be able to work under harsh circumstances or have special requirements regarding the amount of power they need to be able to operate in combination with high-frequency operation.

Although semiconductor products are produced in mass production (and therefore cannot be considered CoPS in the strict sense (Hobday, 1998), the development of these products has a number of parallels with the development of CoPS: often, the development of these products is a unique endeavor. In some cases, products are developed that are derivative products of a “platform,” but in many other cases, the product under development requires completely new design and architecture. Therefore, the development of new semiconductor products can be quite complex. In fact, complexity and uncertainty (and the ability of companies to cope with these) are directly related to the success of R&D (that is, the processes in which new technologies are developed and applied to create new products) (West, 2000). In another article, West and Iansiti (2003)observe that two processes are responsible for the accumulation of new knowledge in semiconductor industry R&D: experience and experimentation. The processes are subsequently coupled to particular company strategies, which could strengthen each of these processes. West (2000) identifies three processes in the semiconductor industry that give rise to an increase in complexity, uncertainty, and risk: First, the increasing product complexity also leads to increasingly complex production processes. Because more process steps are necessary to produce a product, the chance of failure in one of these steps also goes up, Second, because semiconductor devices often operate near the limits of what is physically possible, it is often unclear what the maximally attainable performance of a semiconductor device is, so this increases the uncertainty about what the possible performance of a device is at the start of the development. Third, increasingly complex production processes require production facilities that are increasingly complex and therefore increasingly expensive. The increased capital costs for these production facilities give rise to an increased investment risk.

Because of a number of properties of the industry, an interesting parallel can be drawn between the semiconductor industry and another industry: the pharmaceutical industry (Lim, 2004). Although these industries produce products that are quite different in nature, there is an important similarity: the costs of research and development are high and the unit production costs are low. On top of this, both basic and applied research plays an important role in R&D activities in both industries.

In the semiconductor industry, project management is applied extensively to developing new products or processes or performing other tasks. Because new technologies are developed rapidly in the semiconductor industry, the execution time of projects is of critical importance. An insight into the complexity of projects would provide valuable knowledge toward the efficient and effective execution of projects.

∘ The TOE Framework

As was mentioned before, the TOE framework aims to understand the different aspects of project complexity. This framework was developed on the basis of empirical research in the process industry. In the appendix, an overview is given of the TOE framework as it is described in (Bosch-Rekveldt, 2011; Bosch-Rekveldt, Jongkind, et al., 2010). The TOE framework serves as the basis of this research project. The TOE framework consists of 47 elements; each describes a single aspect of complexity that can occur in a project. The elements are divided into three categories of complexities: technical (17 elements), organizational (16 elements), and external (14 elements). What these complexities actually entail can be seen in the appendix to this article. Technical complexities are related to the technical content of a project, such as goals, scope, tasks, experience, and technical risk; organizational complexities are related to the organizational aspects of a project, such as resources, the project team, trust, and organizational risk; external complexities are complexities that are related the environment in which the project is executed, such as stakeholders, location, market conditions, and external risks.

One of the goals of this research project is to see whether the existing TOE framework would fit the current practice at a semiconductor company. The outcome of the research should then give indications of the types of complexities that semiconductor companies encounter in their projects.

∘ Research Questions

The above considerations led to the following set of research questions:

  1. Which factors play a role in the complexity of projects in a semiconductor company?
  2. What is the applicability of the TOE framework to grasp project complexity in projects in a semiconductor company?

▪ Methods

The empirical part of this research project consisted of 16 qualitative case studies of projects that are carried out at the semiconductor company. For each case study, we chose as the research strategy to do the empirical part of the research, because our goal was to study a contemporary real-life situation (Yin, 2003). We chose the amount of 16 cases to get a good balance between a broad overview of project that is executed in the company and the time that was available to execute the research. On top of this, the choice was made to perform one interview per case, to be able to consider a large amount of different cases. Because the interviewee had to have the most extensive knowledge about the project, the project manager was interviewed, who was involved in all of the different aspects of the project. A downside to this approach is that the researcher only gets a single perspective on the case.

The projects that were studied in this research project differed in a number of ways. Two main types of projects under study can be distinguished in this research project: product development projects and process development projects. Furthermore, projects from different functional areas within the company were studied (these functional areas differ in the types of applications for which products are developed). From the 16 investigated projects, some were finished, some were unfinished (albeit in an advanced stage), and some were terminated.

Data collection

Interviews of approximately 60 minutes were held with the interviewees. To ensure data reliability, the interviews were recorded, next to the notes that were taken during the interview. A worked out version of the interview was sent back to the interviewee to ensure that the right information came across. During the interviews, the interviewees were asked about the content of the project under study and about their views on project complexity (i.e., project complexity in the semiconductor industry in general, not limited to the single project that was treated in the interview). Subsequently, the interviewees were introduced to the TOE framework and were asked to fill in a scoring chart of the TOE framework, related to the project under study. The interviewees were asked to give a score to each complexity in the framework, related to the applicable of that complexity to the project. The scores ranked from 1 (least applicable) to 5 (most applicable). If a complexity was completely non-applicable to the project, the interviewee could also give an NA (not applicable) mark. After filling out the framework, the interviewees were asked if they missed any complexities in the framework, and if this was the case, which complexities could be added to improve the framework.

Data analysis

Two types of data analyses were done on the basis of the information that was gathered during the interviews: intra-case and inter-case analyses (Yin, 2003). In the intra-case analyses, each case was analyzed on its own. In the inter-case analyses, different cases were compared along a number of dimensions. The goals of these different types of analyses were to get an overview of the characteristics of single cases and to observe patterns of differences or similarities between cases. Examples of questions that were asked in the intra-case analyses were:

  • - What project complexities are mentioned?
  • - Which aspect of project complexity has the largest influence on the total complexity of the project?
  • - What are the background and work experience of the interviewees?

Examples of questions that were asked in the inter-case analyses were:

  • - What kinds of projects have been studied in the cases?
  • - What are the main drivers behind the studied cases?
  • - What causes projects to be complex, according to the interviewees?
  • - Which project complexities are mentioned in the cases?
  • - Can a hierarchy of complexities be established? (e.g., through the number of times that a certain complexity is mentioned in the different interviews)

The answers to these questions are presented in the results section of this article.

Limitations

The approach taken has a number of limitations: Because a limited number of cases have been studied in this research project, one could ask the question whether this number of cases is sufficient to draw general conclusions from the data gathered. We tried to make this limitation as small as possible by carefully choosing a set of projects that covers a broad number of subjects in which the company is active. To ensure construct validity and reliability of the findings, all interviews have been recorded and the worked out interviews were sent back to the interviewees for review (reference check method). This way, we could make sure that what we wrote down was what the interviewee was thought to have said.

· Results

In this section, the results and analyses of the empirical part of this research project are presented (compare these parts with the questions posed in the preceding section). First, we will cover the backgrounds of the interviewees who were parts of this research project; second, we will shortly discuss the different project types that we encountered during the research; third, the different drivers behind the projects are discussed; fourth, an overview is given of the complexities that were mentioned by the interviewees before they were introduced to the framework; fifth, the scores of the elements in the TOE complexity framework are presented; and, finally, the elements that were lacking in the original framework according to the interviewees are listed.

Interviewee backgrounds

All project managers who were interviewed in this research project had a technical background: they all started in an engineering role (project team member) before obtaining the role of project manager. Out of the 16 interviewees, 12 interviewees had a background in electrical engineering or related field, 3 interviewees had a background in physics, and 1 interviewee had a chemical engineering degree. This observation could have profound implications for a possible application of the framework at the company to assess project complexity, such as the particular way of implementation of the framework (e.g., types of questions that are asked in a complexity assessment).

Project types encountered

In general, a distinction can be made between two types of projects in this research project: product development projects and process development projects. In a product development project, the main goal is to develop a new product (this can be the development of a completely new conceptual product or the further development of an existing product). Because the fabrication process of semiconductor devices consists of many steps, the processes that make up this fabrication process are continuously being improved. The process improvements or developments are often done in the form of a process development project.

Concerning the product development projects that were studied, we can make a number of interesting observations. An important factor in the development of new semiconductor products is the type of design effort that is necessary to create such a product: the nature of the design can either be digital (where the signals that the device processes are zeros and ones), analogue (where the signals that the device processes are continuous; i.e., these signals can take on a range of different values), or mixed-signal (in this case, digital and analogue signals are both processed on the device). In general, digital products are easier to design and model than analogue products. Each type of design asks for a different approach and for different areas of expertise to be present in the design team.

Although the chip design is an important step in the development process, it is certainly not the only step that is taken to deliver a successful product. Because each device needs to be tested after it is manufactured, test engineers develop industrial tests to determine that each chip works. Developing these specialized tests for each new device is a part of the development project. Also, an industrial mass-manufacturing method needs to be developed to produce the chips at the lowest possible unit cost. The attentive reader might note that the different expertise areas that are involved in the project might also have conflicting interests. It is therefore the challenge to the project manager to bring these together. One interesting aspect of project complexity that came forward from the interviews is the availability of simulation models to correctly predict the behavior of the product under operating conditions. On one hand, the manufacturing of prototype models is an expensive exercise, so simulating the behavior of a device would save costs. On the other hand, the right simulation models are not always present, because a device can operate in a parameter regime that is not covered by existing models. The absence of the right models is therefore a source of complexity in semiconductor projects where devices are developed that work in new regimes. This aspect of complexity is also described in general by the TOE framework (“uncertainty in methods,” TT4).

The other type of projects that was encountered during the case studies is the process development project. In total, four projects of this type were studied in this research. Although the number of projects of this type was small, a number of observations can be made: one aspect of these projects that caught our attention was the fact that external expertise (outside of the project team) is often involved in the development of new processes. Furthermore, customers are often involved in this type of project, although these projects do not directly result in a new product. Both of these factors have an influence on the complexity of the project and can be found in the TOE framework.

Project drivers

During the interviews, the project managers were asked to rank the project drivers (project cost, schedule, and specifications) in order of importance with respect to the project under study, where 1 is most important and 3 is least important. The outcome of this measurement is shown in Table 1.

Table 1: An overview of the average ranks and standard deviations of project drivers in the interviews.

N=16 Specifications Schedule Project cost
Average rank 1.375 1.675 3
Standard deviation 0.5 0.5 0

We can observe that specifications and schedule play the most important roles as project drivers, and project cost is of less importance in all the projects that were studied. Project costs play a relatively small role, because development costs are a small fraction compared with the total costs (including manufacturing costs) that are involved in the production of the product.

TOE scores

After an introduction to project complexity, where the interviewees were asked about their perception of project complexity, the interviewees were confronted with the existing TOE framework. This resulted in 16 filled-out versions of the TOE framework, in which the applicability of each element to the respective projects was indicated (1 to 5; the higher, the more applicable). In the appendix, an overview is given of the scores of the different elements in the case studies. In this section, we will highlight three different results: high scoring elements, low scoring elements, and elements that had a high number of “not applicable” scores given.

The elements that had high scores (scoring 4.00 or more on average) were (in parentheses, the element code, mean score, and standard deviation are given):

  • - High project schedule drive (ORE1, 4.44 (0.63))
  • - Level of competition (EM3 4.07 (0.59))
  • - Involvement of different technical disciplines (TT5, 4.06 (0.68))
  • - Technical risks (TR1, 4.00 (1.15))
  • -

These elements reflect the challenges with which the project teams have to cope: the teams need to consist of team members of different backgrounds (which can introduce communication problems); the products that are developed are often very innovative, which means that a certain amount of uncertainty exists about whether the developed product can live up to the needed specifications; there is a large amount of pressure from competitors, which means that new products need to be developed fast.

The elements that had low scores (scoring 1.50 or less on average) were (between parentheses, the element code, mean score, and standard deviation are given):

  • - Number of financial sources (ORE6, 1.17 (0.58))
  • - Trust in the project team (OT1, 1.50 (0.97))
  • - Trust in contractor (OT2, 1.22 (0.44))

One has to take notice that the fact that these elements scored low values does not mean that these elements are unimportant: it might very well be that these elements scored low scores, because the company took sufficient measures to control the influence that these elements have on the different aspects of complexity of the projects.

A number of elements had higher “not applicable” scores than other elements. The following elements had “not applicable” scores of more than 75%:

  • - Lack of HSSE awareness (ORE4, 75% NA)
  • - Presence of a JV (joint venture) partner (OP3, 88% NA)
  • - Political influence (ES4, 81% NA)
  • - Required local content (ES6, 81% NA)
  • - Weather conditions (EL2, 88% NA)
  • - Remoteness of location (EL3, 81% NA)

From these elements, one element deserves special attention: Presence of a JV partner (OP3). Although this element was deemed to be not applicable in 14 out of the 16 cases, there was a joint venture partner present in two projects, and the presence of the JV partner in these projects did contribute toward the total complexity of the project. Hence, the presence of a JV partner is still an important element that possibly can have an influence on the (organizational) complexity of a project.

Missing elements

Before the interviewees were introduced to the TOE framework, they were asked if they could mention a number of complexities that could be present in projects in the semiconductor industry. In addition, to see whether the framework sufficiently describes the complexity of projects at the company, interviewees were asked to mention complexities they encountered in their experience in projects, but that the framework did not (sufficiently) describe. Below, we will give an overview of the extra elements that were mentioned at least by three different project managers (independently from each other, before or after the interviewees were introduced to the framework):

  • - New combinations of technologies (mentioned four times)
  • - Multiple companies/departments in a single team (mentioned three times)
  • - New market (mentioned three times)
  • - The availability of modeling tools (mentioned three times)
  • - The amount of (design) reuse; more reuse means less design effort (mentioned three times)
  • - Change of customer requirements/relation (mentioned three times)

Discussion

Obviously, the results are very much dependent on the selected project sample for the case studies. The case studies resulted in a number of elements that were most prominent in the 16 investigated cases. Apart from the element high schedule drive, the highest scoring elements were less prominent in the studies performed in the process industry (Bosch-Rekveldt, 2011). The lowest scoring elements and those indicated with “not applicable” are not that surprising; just compare the context of typical semiconductor industry projects with the context of typical process industry projects. The missing elements, according to the interviewees, then make us pose the question of whether or not to include these in a next version of the TOE complexity framework. At this stage, however, we would only recommend to add these elements for particular use in the semiconductor industry.

· Conclusions

During the interviews, we have seen that the projects are almost always executed in multidisciplinary teams. The reason for this is the wide variety of expertise areas that are necessary to develop a new product or process in the semiconductor industry. Moreover, teams are often dispersed over different locations, sometimes even over different continents. This dispersion has profound implications on the way different team members can communicate with each other. Because there is constant market pressure on the company, the company needs to develop innovative product constantly. This research project showed that the main drivers behind the project are the specifications of the project and the schedule. A good support structure from within the company is needed to be able to efficiently and effectively develop new products and processes, which we have seen in the case studies.

If one looks at the different complexities that play a role in the projects that were studied, one could say that, in general, technical complexities play a relatively large role. In particular, the technical complexity of the product that is being developed in a project (e.g., the ability to model the behavior of a product under development before a test product is made) and the influence of the market on the project play a large role in the complexity of the project.

We have seen that not all elements that are parts of the TOE framework are equally important to the current practice in the semiconductor company. On the whole, however, the framework seems to describe complexities that are encountered in the projects quite well.

· Further Research

Although preliminary conclusions can be drawn from the TOE scores, these scores were still obtained from a relatively small number of cases; therefore, one has to be very careful drawing general conclusions from these outcomes.

The case studies that have been performed have yielded results about the ways in which complexities influenced these cases and to which degree each complexity had an influence on the projects. Because 16 projects have been investigated in this research project, more research could also be done on more cases within other companies. This would allow the researcher to see if the results that were found in this research project are also more generally applicable. A second strategy used to test the validity of the framework to semiconductor projects would be to set up a survey, whereby a larger number of projects could be investigated.

Applying the framework in the front-end phase of a semiconductor project could provide benefits to the company. In the case studies and in the literature, we have seen that complexities can have a large influence on the project's execution and success. A tool that could assess the complexity and sources of complexity of a project would be valuable to a company. Certain tools that do this already exist, but these are mainly concentrated on the design complexity of a new product. The framework that we have used in this research project would be applicable to a larger class of (development) projects. For further application of the framework in practice, the amount of effort that would be needed from the project manager to fill in the framework would need to be assessed. The main benefit of the use of the framework would probably be the estimation of the types of complexities that are encountered during a project. Of course, it is also important to assess whether the extra effort that is needed from the project manager and project team pays off against the possible gains of using a complexity framework. It would also be interesting to look at the dynamics of project complexity: how project complexity and the perception of project complexity change during the course of a project.

As was brought forward in the literature study, interesting parallels can be drawn between the semiconductor industry and the pharmaceutical industry; therefore, it would be very interesting to perform the same research exercise in that industry as well. This way, one could see whether parallels can also be found in project complexities that are encountered in this industry.

Appendix: Elements of the TOE Framework

Technical elements:

Sub-ordering Element code Element
Goals TG1 Number of project goals
Goals TG2 Non-alignment of project goals
Goals TG3 Unclarity of project goals
Scope TS1 Uncertainties in scope
Scope TS2 Strict quality requirements
Scope TS3 Project duration
Scope TS4 Size in CAPEX (CAPital EXpenditure)
Scope TS5 Number of locations
Experience TE1 Newness of technology (worldwide)
Experience TE2 Experience with technology
Tasks TT1 Number of tasks
Tasks TT2 Variety of tasks
Tasks TT3 Dependencies between tasks
Tasks TT4 Uncertainty in methods
Tasks TT5 Involvement of technical disciplines
Tasks TT6 Conflicting norms and standards
Risk TR1 Technical risks

Organizational elements:

Sub-ordering Element code Element
Resources ORE1 High project schedule drive
Resources ORE2 Lack of resource and skills availability
Resources ORE3 Lack of experience with parties involved
Resources ORE4 Lack of HSSE awareness
Resources ORE5 Interfaces between different disciplines
Resources ORE6 Number of financial sources
Resources ORE7 Number of contracts
Project team OP1 Number of different nationalities
Project team OP2 Number of different languages
Project team OP3 Presence of a JV partner
Project team OP4 Involvement of different time zones
Project team OP5 Size of the project team
Methods OM1 Incompatibility between different project management methods/tools
Trust OT1 Trust in project team
Trust OT2 Trust in contractor
Risk OR1 Organizational risks

External complexities:

Sub-ordering Element code Element
Stakeholders ES1 Number of external stakeholders
Stakeholders ES2 Variety of external stakeholders’ perspectives
Stakeholders ES3 Dependencies on external stakeholders
Stakeholders ES4 Political influence
Stakeholders ES5 Lack of company internal support
Stakeholders ES6 Required local content (forced cooperation with local parties)
Location EL1 Interference with existing site
Location EL2 Weather conditions
Location EL3 Remoteness of location
Location EL4 Lack of experience in the country
Market conditions EM1 Company's internal strategic pressure
Market conditions EM2 Instability of the environment
Market conditions EM3 Level of competition
Risk ER1 Risk from environment

Element scores per category:

Element code Score (SD) N (%NA) Element code Score (SD) N (%NA) Element code Score (SD) N (%NA)
TG1 2.69 (1.20) 16 (0) ORE1 4.44 (0.63) 16 (0) ES1 2.33 (1.29) 15 (6)
TG2 1.87 (1.06) 15 (6) ORE2 3.38 (1.15) 16 (0) ES2 2.00 (1.00) 15 (6)
TG3 1.81 (1.17) 16 (0) ORE3 2.67 (1.23) 15 (6) ES3 2.43 (1.40) 14 (13)
TS1 2.00 (1.26) 16 (0) ORE4 1.00 (0.00) 4 (75) ES4 1.00 (0.00) 3 (81)
TS2 3.31 (1.58) 16 (0) ORE5 2.31 (1.20) 16 (0) ES5 1.56 (1.09) 16 (0)
TS3 3.38 (0.96) 16 (0) ORE6 1.17 (0.58) 12 (25) ES6 1.33 (0.58) 3 (81)
TS4 3.56 (1.26) 16 (0) ORE7 1.90 (1.20) 10 (38) EL1 3.19 (1.11) 16 (0)
TS5 2.94 (1.48) 16 (0) OP1 3.47 (1.13) 15 (6) EL2 1.00 (0.00) 2 (88)
TE1 3.19 (1.52) 16 (0) OP2 2.00 (1.00) 15 (6) EL3 1.00 (0.00) 3 (81)
TE2 2.81 (1.33) 16 (0) OP3 3.50 (0.71) 2 (88) EL4 1.57 (1.13) 7 (56)
TT1 3.56 (1.21) 16 (0) OP4 2.40 (1.12) 15 (6) EM1 3.19 (1.60) 16 (0)
TT2 3.63 (0.96) 16 (0) OP5 3.44 (1.09) 16 (0) EM2 1.54 (0.97) 13 (19)
TT3 3.31 (1.20) 16 (0) OM1 1.67 (1.11) 15 (6) EM3 4.07 (0.59) 15 (6)
TT4 3.31 (1.20) 16 (0) OT1 1.50 (0.97) 16 (0) ER1 2.31 (1.35) 16 (0)
TT5 4.06 (0.68) 16 (0) OT2 1.22 (0.44) 9 (44)
TT6 1.58 (0.90) 12 (25) OR1 2.81 (1.38) 16 (0)
TR1 4.00 (1.15) 16 (0)

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