| AVS 60th International Symposium and Exhibition | |
| Plasma Science and Technology | Wednesday Sessions |
| Session PS-WeA |
| Session: | PSTD at AVS60: Looking Back and Moving Forward |
| Presenter: | M.J. Kushner, University of Michigan |
| Correspondent: | Click to Email |
Modeling of plasma processes has significantly advanced during the tenure of the AVS with benefits to investigating fundamental science issues and to technology development. Modeling’s contributions to plasma processing science have been facilitated by a series of milestone contributions, including development of accessible particle-in-cell simulations, use of molecular dynamics for investigation of surface processes, hybrid techniques which have expanded the variety of plasmas investigated, multi-phase models for dusty plasmas, technology relevant profile simulation, on-demand computation of cross sections, and now state-of-the-art algorithms embedded in commercially available modeling platforms. Although model development has been closely tied to applications, a collaborative development of fundamental theories has been exceedingly important to formulating proper and relevant technology focused models. The variety and dynamic range of plasma processing applications, from low pressure magnetrons to atmospheric pressure jets and now to liquids, has both challenged and benefited modeling. In other fields of applied physics, and other sub-fields of plasmas, the dynamic range of interest is markedly smaller and so resources have been concentrated on advancing modeling in more focused areas. Plasma processing, with its greater dynamic range, has been less focused with the unexpected benefit of finding more common ground between what appears to be quite different sub-fields of plasma processing. With this virtual capability, the plasma processing community has embraced computational experimentation as a necessary and beneficial tool. In this talk, a perspective will be provided of modeling’s impact on science and technology development in plasma processing, and on future opportunities.
*Work supported by the Semiconductor Research Corp., DOE Office of Fusion Energy Science, National Science Foundation, Agilent Research Labs and HP Research Labs.