Presentation on theme: "Paper review: Fractional Order Plasma Position Control of the STOR-1M Tokamak Outlook of FOC in Plasma Etching: Challenges and Opportunities Zhuo Li PhD."— Presentation transcript:
Paper review: Fractional Order Plasma Position Control of the STOR-1M Tokamak Outlook of FOC in Plasma Etching: Challenges and Opportunities Zhuo Li PhD student, Dept. of ECE, USU.
References . Shayok Mukhopadhyay, YangQuan Chen, Ajay Singh and Farrell Edwards, “Fractional Order Plasma Position Control of the STOR-1M Tokamak”, 48th IEEE CDC, Dec, 2009, pp . Mukhopadhyay, Shayok, "Fractional Order Modeling and Control: Development of Analog Strategies for Plasma Position Control of the Stor-1M Tokamak" (2009). All Graduate Theses and Dissertations. Paper 460. [online available], . M. Emaami-Khonsari, “Modelling and Control of Plasma Position in the STOR-M Tokamak,” Ph.D., University of Saskatchewan, Saskatoon, April . Shane Lynn, “Virtual Metrology for Plasma Etch Processes”, PhD thesis, Electronic Engineering Department, National Univ. of Ireland.  John V. Ringwood, Shane Lynn, Giorgio Bacelli, Beibei Ma, Emanuele Ragnoli, and Sean McLoone, “Estimation and Control in Semiconductor Etch: Practice and Possibilities”, IEEE TRANS ON SEMICONDUCTOR MANUFACTURING, VOL. 23, NO. 1, FEBRUARY 2010 . Lynn Fuller, “Plasma Etching”, [lecture slides], Microelectronic Engineering, Rochester Institute of Technology. . Henri Janseny, Han Gardeniers, Meint de Boer, Miko Elwenspoek and Jan Fluitman, “A survey on the reactive ion etching of silicon in micro-technology”, J. Micromech. Microeng. 6 (1996), 14– 28. . Lab modules, webpage, [online], [Mar ] Slide 2
The Physical System Tokamak: is a device using a magnetic field to confine a plasma in the shape of a “doughnut”. [Wikipedia.org] Slide 3 Fig3-1. The schematic of Tokamak as a transformer. Fig3-2. The STOR-1M Tokamak in USU. 
Bank Current Waveforms B T - Toroidal field bank I Oh - Ohmic heating bank I Ve - Vertical equilibrium bank I Hc - Horizontal compensation bank I Vc - Vertical compensation bank Slide 4 Fig4. The bank current waveforms of STOR-1M. 
Fig5-1. The Plasma position estimation system. Measurement Mechanism Slide 5 Fig5-2. Proposed position estimation approach. 
Plasma Position Modeling Slide 6
ControllerKpKiKdorder FO-PI ZN-PID Fractional Order Controller Slide 7 Table: CONTROLLER PARAMETERS FOR THE STOR-1M TOKAMAK Fig8-1. Position control results.  Controller parameters Results and comparison (on emulator) Fig8-2. Position control results. 
FO-PI controller is better than the ZN-PID controller in terms of response time, control effort and steady state error. Conclusion Slide 8
Plasma etching process in semiconductor manufacturing Etching variables hard to measure Real-time control hard to achieve Measurement technology in the literature  Virtual metrology  Optical emission spectroscopy (OES) Mass spectrometry Plasma impedance monitoring Etc. Outlook-challenges Slide 9 Fig9. OES. 
Plasma Etching - Intro Etching outcome and profile Isotropic (non-directional removal of material from a substrate) Anisotropic (directional) Slide 10 Ideal etch Fig10-1. No process is ideal, some anisotropic plasma etches are close.  Poor etch Fig10-2. One-run multi-step RIE process. Top left: after anisotropic etching the top Si of an SOI wafer. Top right: after etching the insulator and sidewall passivation. Middle left: during isotropic etching of the base Si. Middle right: after isotropic etching the base Si. Bottom: typical finished MEMS products. 
Controls in the Literature Slide 12 Fig12. Etch tool control possibilities with information flow. 
Controls in the Literature Slide 13 Run-to-Run (R2R) Control [a],[b],[c]. Predictive functional control . Neural network control Etc. [a], M. Hankinson, T. Vincent, K.B. Irani, and P.P. Khargonekar. Integrated real-time and run-to-run control of etch depth in reactive ion etching. IEEE T. Semiconduct. M., 10(1): , Feb [b]. X.A. Wang and R.L. Mahajan. Articial neural network model-based run-to-run process controller. IEEE Trans. Components, Packaging, and Manufacturing Technology, Part C., 19(1):19-26, Jan [c]. J.P. Card, M. Naimo, and W. Ziminsky. Run-to-run process control of a plasma etch process with neural network modelling. Qual. Reliab. Eng. Int., 14(4): , 1998.
Data Outlook-Opportunities Slide 14 Fig10. Endpoint mono-chromtor output over four preventative maintenance (PM) cycles. 
Other efficient “learning machines” RVM Other fitting methods TLS fitting for “data boxes” (not point) Interval computation tools (IntLab) Dynamic VM – R2R VM Fractional Order ANN based VM Neuronal dynamics is inherently “fractional order” Fractional order iterative learning control Cognitive process control Slide 15 Slide from Dr.Chen’s Lam Research Talk Outlook-Opportunities
Dynamic Virtual Metrology in Semiconductor Manufacturing Outlook-Opportunities Slide 16 Slide from Dr.Chen’s Lam Research Talk