Success Story

Successful scaling of an R&D Team

Dr. Femke Tabak, Dr. Dirk Ortloff, Dr. Jens Popp

June 10, 2016

Louis Couperusplein 2 • 2514 HP, Den Haag • The Netherlandshttp://www.appliednanolayers.com

agile • efficient • reliable

camLine

Industriering 4a • 85238 Petershausen • GermanyTel.: +49 (8137) 935-0 • Fax: +49 (8137) 935-235

http://www.camline.com

Successfully scaling up an R&D team is a challenge, the amount of information anddata from machines, planned and executed experiments and result analysis increasesvery rapidly as more people become involved. To efficiently coordinate workflows,and to reduce the time needed to integrate new knowledge into the production chain,it is important to invest in the structured dissemination and automation of results,documentation and core knowledge from an early stage.

Applied Nanolayers recognized the need to integrate their R&D activities early intheir collaboration. After investigating several different solutions, they decided toadopt the XperiDesk Process Development Execution System. This enabled seamlesscollaboration, communication, documentation and knowledge management.

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1 The Challenges

1 The Challenges

Scaling up from a one man project to a real start-up company, bringing in new people and equip-ment is a challenge for everyone involved.

At Applied Nanolayers we faced this task when setting up our graphene production foundry.We are a start-up company focusing on the production of 2D materials, primarily graphene andboron nitride. As we want to deliver solely the highest quality material, we invested heavily inperfecting our production processes and want to keep track of all process steps and material qual-ification activities.

We started with a small team. At the start, one person worked on developing the first versionof our production process on a prototype of our production tool(s). As we expanded, this projectquickly increased to more people and tools for the development of the graphene production pro-cess. More people had to work on post-processing of the graphene wafers. Having started with avery small team and budget, we faced some major problems for our growth.

On the one hand, with more people it gets more difficult to keep everyone on the same track.Questions like “What were the settings of that experiment?” or “Where are the result images of…?” came up more often than we liked. Because the equipment we use in development had littleautomation or interfaces, most data collection had to be done, manually. That resulted in a lot offiles and numbers on our notebooks (the electronic and paper ones) and on our server. While thisworked for the initial experiments, it became more challenging as we grew. It was no longer onlyone person who needed access to our data, the whole team also needed the results. We wouldhave risked that our experiments were repeated again and again. Only by documenting our workit was possible for us to collaboratively learn.

On the other hand, as a start-up we needed money for continuous growth which comes in ourcase from venture capital. We were obliged to report our progress, our successes and occasionally,our failures. Our investors and auditors required from us to provide oversight of the executedwork as well as holistically capture the IP generated from our experimentation without relying onpure personal knowledge.

For these reasons we decided at any early stage that we need to tackle these problems at the be-ginning before we lose valuable data and learnings. Finally, we ended up in looking for a softwaresolution that could fulfill our requirements:

1. Enable multiple users to work on the technology development2. Support the transition from R&D to a production process without loss of flexibility needed

in R&D3. Collect all the process steps performed on the wafers4. Collect all the analyses results together in a way that would allow us to trace all data and

group it according to different process settings and results5. Identify and solve the crucial process parameters6. Generate reports on performed work7. Provide shared access to all experiments, results and learnings

Especially the second point was important to us. While there are many systems coping withthese tasks in production, most software tools are too inflexible to be applied in an R&D envi-ronment. Processes in R&D are changing daily or even hourly. We needed a system that could

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2 Solution approach

keep pace with these changes. The result data are very diverse. While some of our results arestructured data like numbers, others are images or just result files from equipment that need to beinterpreted.

2 Solution approach

We were investigating different software solutions and found some tools that provided partial ful-fillment of our requirements. Some were good at design but lacked the tracking ability. Others hadgood tracking of documents but failed in managing our production sequences. With XperiDeskwe found a solution that offered both. We were delighted to find a solution that addresses R&Drather than just volume production.

Implementation was done in several steps. Our first aim was to document our historical data.We used the design part of XperiDesk and reentered our process flows. The simple drag&dropas well as the very flexible management of parameters and units made this task easy and efficient(see figure 1)1.

Figure 1: Editing manufacturing flows

As a second step, we started to accumulate our result files in XperiDesk. After doing somemanual work to sort our files in a logical structure, we were able to load them into XperiDesk withthe file loading client. This client allowed us to extract wafer, lot and experiment names from thepath and filename of the image files. Together with definable additional data these names wereused to create the corresponding items in the system. Wafers, lots and experiments where createdautomatically. The images were imported and related to the corresponding item as presented infigure 2. As a result, we have had most of our history documented and accessible from multipleperspectives in XperiDesk. With these measures in place, we could start to document our currentwork better while still having the reference to our history.

1 To protect ANL IP, all screenshots in this document represent demonstration data of camLine in structures similar tothe ones at ANL

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2 Solution approach

Figure 2: Structure of the wafers repository related with the result files and manufacturing se-quence

Next, we had to document the current work “live”. We used the design part of XperiDesk todevelop our new experiments. We managed the parameter settings for our tools as well as theorder of steps to be performed. After this, we assigned (or re-assigned in the case of reuse) thewafers to the flows and created process runs. XperiDesk provided the capability to do split runsfor our DoE’s (see figure 3).

The real execution was the next phase. We started using the XperiDesk operator client (seefigure 4) which provides a very simple web-based user interface for the manual data collection onour tools. We are able to track results per wafer and step and to show them immediately in thefull client. We could share the progress with all team members without sending emails when astep was done.

It is important for us to implement a continuous process of improvement. Quality needs to bedeveloped and persistently checked by inspection. For this purpose the equipment was adaptedto our needs and delivered image files. These were stored on the equipment interface computer.To get the files into XperiDesk, we used again the loading client. The client runs on the equipmentinterface PC, it grabs all files in regular intervals and uploads them into XperiDesk. By using anaming convention, we assure that they are linked to the correct wafers.

Finally, we used the XperiDesk search and reporting tool to perform analyses on our processes.It is now easy for us to see the history of our wafers in production or rework. We can also seewhich equipment was used in what settings for which experiment and were able to look at ourdata from different angles. Using the report generator we could do queries on our data and exportthem to e.g. Excel for further evaluation or graphical representation. The camLine support teamhelped us to establish some direct database reports for our progress reporting.

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2 Solution approach

Figure 3: A DoE graph in XperiDesk

Figure 4: XperiDesk Operator client

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3 The results

Figure 5: Search results with the steps and results in Excel

3 The results

By implementing XperiDesk we have a Process Development Execution System available thathelps us to keep track of all our executed experiments. Additionally, all data files and imagesare collected and backed up in one central place. We use a system to know the status of our workin progress as well as our historical work.

It is most advantageous to us that XperiDesk is designed for R&D. We can change our processeson the fly, even if processing has already started. Creating new process flows is done via drag anddrop and reordering or exchanging steps is easy. Flexibility also appears in the data collection.While we can define data collection plans for each step (as result parameters to be collected viathe operator client), we are not fixed to that plan for each run. We can add or remove parametersas we need them.

The document management capability is another important feature to us. We are now able tobrowse for result files from different angles. The search could start from a wafer, an experimentor a process run. And even better, we see the complete context. We can, for instance, look at alldata collected in one run, lot or experiment.

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4 Summary

Figure 6: A relation graph in XperiDesk

4 Summary

We have successfully scaled up from a prototype graphene growth process on 2” wafers to runningproduction on 8” wafers. This transition has been smooth due to the complete and insightful datastorage of both, experiments and analyses in the XperiDesk database. In addition, we can easilyadd new processes as well as processes that are still under development to the database. Thisallows us to expand our materials portfolio while keeping track of all the experiments and testsdone in the process.

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