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3. Production Process Characterization
3.1. Introduction to Production Process Characterization
3.1.3. Terminology/Concepts

Process Models

Black box model and fishbone diagram As we will see in Section 3 of this chapter, one of the first steps in PPC is to model the process that is under investigation. Two very useful tools for doing this are the black-box model and the fishbone diagram.
We use the black-box model to describe our processes We can use the simple black-box model, shown below, to describe most of the tools and processes we will encounter in PPC.  The process will be stimulated by inputs. These inputs can either be controlled (such as recipe or machine settings) or uncontrolled (such as humidity, operators, power fluctuations, etc.). These inputs interact with our process and produce outputs. These outputs are usually some characteristic of our process that we can measure. The measurable inputs and outputs can be sampled in order to observe and understand how they behave and relate to each other.
Diagram of the black box model black box model describing process
These inputs and outputs are also known as Factors and Responses, respectively. 
Observed inputs used to explain response behavior (also called explanatory variables). Factors may be fixed-level controlled inputs or sampled uncontrolled inputs. 
Sampled process outputs. Responses may also be functions of sampled outputs such as average thickness or uniformity. 
Factors and Responses are further classified by variable type We further categorize factors and responses according to their Variable Type, which indicates the amount of information they contain. As the name implies, this classification is useful for data modeling activities and is critical for selecting the proper analysis technique. The table below summarizes this categorization. The types are listed in order of the amount of information they contain with Measurement containing the most information and Nominal containing the least.
Table describing the different variable types
Type Description Example
Measurement discrete/continuous, order is important,  infinite range particle count, oxide thickness, pressure, temperature
Ordinal discrete, order is important, finite range run #, wafer #, site, bin
Nominal discrete, no order, very few possible values good/bad, bin, high/medium/low, shift, operator
Fishbone diagrams help to decompose complexity We can use the fishbone diagram to further refine the modeling process. Fishbone diagrams are very useful for decomposing the complexity of our manufacturing processes. Typically, we choose a process characteristic (either Factors or Responses) and list out the general categories that may influence the characteristic (such as material, machine method, environment, etc.), and then provide more specific detail within each category. Examples of how to do this are given in the section on Case Studies.
Sample fishbone diagram example of a fishbone diagram decomposing complexity of a
manufacturing process
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