Challenge and Selection

My challenge was to design an optimal processing station for a new chamber, which needs to be capable  of running many different applications.  The different application have different requirements and  emphasize different aspects of performance differently.  This means that certain characteristics of the  station, for example flow and temperature characteristics, are more important for certain applications  than others.  In some cases those requirements are conflicting or inversely related, so it involves making  tradeoffs.  My goal was to create the minimum number of configurations of process stations to satisfy  the various requirements.  Initially a lot of analysis was done, which generated various numerical values  and individual graphs.  After compiling much of this data I decided that more visualization would be the  best tool to use, because it can graphically assist in mapping the requirements, outputs, and capabilities,  while highlighting gaps.  It not only easier for me to see, but it also made it easier to explain to others.

Application

I used visualization by creating a 3D map of three of the most important characteristics and mapping  where the different designs sit in the three dimensional space of those requirements.  In addition I  mapped the expected requirements of prospective application into the same space, which allowed  everyone to see where the designs match up and where there are gaps.

Insight

I learned a new tool for clearly showing requirements and design tradeoffs when the number of  dimensions is low enough.  In this case it was possible to simplify down to the three most important  characteristics.  It showed me where I needed to focus my design efforts and where an optimal solution  might be found, with this solution being the best combination of performance in different areas.

Approach

I learned that this technique can be used earlier in the process to guide design before significant design  activity has been undertaken.  It could be used to see from the beginning whether there is an optimal  design for all known applications or if multiple designs are needed.  My current use of the tool showed  later in the process where there gaps based on the designs that were already done.  It could be done  more efficiently if the tool is used earlier.


Challenge and Selection My challenge was to design an optimal processing station for a new chamber , which needs to be capable of running many different applications . The different application have different requirements and emphasize different aspects of performance differently . This means that certain characteristics of the station , for example flow and temperature characteristics , are more important for certain applications than others . In some cases those requirements are conflicting or inversely related , so it involves making tradeoffs . My goal was to create the minimum number of configurations of process stations to satisfy the various requirements . Initially a lot of analysis was done , which generated various numerical values and individual graphs . After compiling much of this data I decided that more visualization would be the best tool to use , because it can graphically assist in mapping the requirements , outputs , and capabilities , while highlighting gaps . It not only easier for me to see , but it also made it easier to explain to others . Application I used visualization by creating a 3D map of three of the most important characteristics and mapping where the different designs sit in the three dimensional space of those requirements . In addition I mapped the expected requirements of prospective application into the same space , which allowed everyone to see where the designs match up and where there are gaps . Insight I learned a new tool for clearly showing requirements and design tradeoffs when the number of dimensions is low enough . In this case it was possible to simplify down to the three most important characteristics . It showed me where I needed to focus my design efforts and where an optimal solution might be found , with this solution being the best combination of performance in different areas . Approach I learned that this technique can be used earlier in the process to guide design before significant design activity has been undertaken . It could be used to see from the beginning whether there is an optimal design for all known applications or if multiple designs are needed . My current use of the tool showed later in the process where there gaps based on the designs that were already done . It could be done more efficiently if the tool is used earlier .