Fellowship - Artificial intelligence (AI) technologies on the modeling of the relationship between manufacturing process, material factors and battery performance in rechargeable battery production

Job Reference: 000059

Location: Japan

Closing Date: 31/12/2018

Job Posted Date: 28/09/2018

Salary: C10M JPY

Employment Type: Toshiba Fellowship

Business Type: Research & Development

Toshiba uses simulation to predict performance during the planning and design of its lithium ion rechargeable battery (LiB). Separately, at the manufacturing stage, we collect both yield data and process control data using IoT technologies.  However, the relationship between battery efficiency and the manufacturing process is complicated and it remains the case that it is difficult to adequately connect them logically. Therefore, we would like to design a system which measures at the earliest stages the relationship between battery performance and the manufacturing process.
At the start of the project to build this system, AI will be applied to data acquired by Toshiba from existing rechargeable battery production, in order to model the relationship among materials, process factors and battery performance. The objective of the project will be to build the methodology for solving the inverse problem, where necessary processes and materials can be predicted based on the battery performance.

Knowledge and Skills Required

  • Knowledge of the manufacturing process and the performance of lithium ion rechargeable batteries
  • Strong interest in data analysis by applying AI, preferably with hands-on experience
  • Strong interest in development of systems to model the whole process using data on lithium ion rechargeable battery production.

Related Papers

[1] T.Kikuchi and M.Ciappa, “Modeling the threshold voltage instability in SiC MOSFETs by multiphonon-assisted tunneling”, Microelectronics Reliability, vol. 58, p.p. 33-38, 2016.
[2] H.Yabuhuhara and A.Miyamoto, “Prediction of low-energy boron profile for ultra-shallow junction formation by hybrid molecular dynamics method”, Japanese Journal of Applied Physics, vol. 55, p. 016503, 2015.


This position is now closed. We are no longer accepting applications for this position.