IPVF Internship – AI for analysis and optimization of material deposition processes

22/12/2021

Function: Master student

Contract Type: Internship

Starting Date: March 2022

Working Place: Palaiseau, France (Paris-Saclay technology cluster)

Duration: 5-6 months

Education: Engineer or Master 2

 

 

IPVF IN BRIEF

Become an actor of the Energy Transition by joining a team driven by innovation and impact to address today’s most decisive challenges.

 

IPVF – Institut Photovoltaïque d’Île-de-France, is a global Research, Innovation and Education center, which mission is to accelerate energy transition through science & technology.
Gathering industrial PV leaders (EDF, TotalEnergies, Air Liquide, Horiba and Riber) and world-renowned academic research organizations (CNRS, Ecole Polytechnique), multi-disciplinary and international IPVF teams conduct research for clean energy technologies. Supported by the French State, IPVF is labelled Institute for Energy Transition (ITE).

 

IPVF at a glance:
• An ambitious Scientific and Technological Program (6 programs divided in 24 work packages): from tandem solar cell technologies to economy & market assessment, state-of-the art characterization, photocatalysis and breakthrough concepts.
• State-of-the-art technological platform (8,000m²): more than 100 cutting-edge equipments worth €30M, located in cleanrooms (advanced characterization, materials deposition, prototypes for fabrication, modelling…).
• High-standard Education program (M.S. and PhD students).

 

 

 

JOB CONTEXT

Material quality and reproducibility assessment is the key to build efficient solar cells. As the stack of materials become more complex, the quality of each layer becomes crucial. At IPVF, the projects have access to a process platform which is in charge to develop new materials and guarantee a baseline for each solar cell technology. This platform offers different kind of processes (evaporation, sputtering, MBE, ALD…) and requires quality assessments and quality control (QAQC). Until now, this QAQC is done by monitoring the process parameters and performing multiple characterization on the deposited materials. This method is time consuming and costly and we would like to explore the capacity of AI to detect accurately the deviation of the processes and help the optimization of materials and process.

 

MAIN MISSIONS

The objective of the internship is to apply machine learning algorithms on a database of vacuum processes including the different process parameters associated with the characterization data of the obtained samples in order to predict the best deposition conditions for obtaining specific materials.

The expected outputs of the stage are :

  • Proof of concept of the benefit of using AI for deposition process optimization and QAQC.
  • Litterature review of AI for deposition processes.

 

 

PROFILE

Knowledge

  • Machine learning
  • Deep learning
  • Statistics
  • Materials/process (optional)

Know-how

  • Prior experience in modeling in Python or R

 

Self-management skills

  • Strong teamwork skills
  • Multi-disciplinary teamwork
  • Proactive and autonomous

 

 

CONTACT

The application must include: cover letter, resume, names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number), ex of computer program developed, an electronic copy of your most significant research publications (journal or conference publication).

Application to be sent to: frederique.donsanti@ipvf.fr and marie.jubault@edf.fr

 

Need a direct line?

Feel free to contact us for more information about our offers.

  • +33(0)1 69 86 58 60
  • contact@ipvf.fr
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