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Paper review: Fractional Order Plasma Position Control of the STOR-1M Tokamak Outlook of FOC in Plasma Etching: Challenges and Opportunities Zhuo Li PhD.

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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:

1 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.

2 References [1]. 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 [2]. 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], [3]. M. Emaami-Khonsari, “Modelling and Control of Plasma Position in the STOR-M Tokamak,” Ph.D., University of Saskatchewan, Saskatoon, April [4]. Shane Lynn, “Virtual Metrology for Plasma Etch Processes”, PhD thesis, Electronic Engineering Department, National Univ. of Ireland. [5] 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 [6]. Lynn Fuller, “Plasma Etching”, [lecture slides], Microelectronic Engineering, Rochester Institute of Technology. [7]. 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. [8]. Lab modules, webpage, [online], [Mar ] Slide 2

3 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.[1] Fig3-2. The STOR-1M Tokamak in USU. [1]

4 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. [1]

5 Fig5-1. The Plasma position estimation system.[3] Measurement Mechanism Slide 5 Fig5-2. Proposed position estimation approach. [3]

6 Plasma Position Modeling Slide 6

7 ControllerKpKiKdorder FO-PI ZN-PID Fractional Order Controller Slide 7 Table: CONTROLLER PARAMETERS FOR THE STOR-1M TOKAMAK Fig8-1. Position control results. [3] Controller parameters Results and comparison (on emulator) Fig8-2. Position control results. [3]

8 FO-PI controller is better than the ZN-PID controller in terms of response time, control effort and steady state error. Conclusion Slide 8

9 Plasma etching process in semiconductor manufacturing Etching variables hard to measure Real-time control hard to achieve Measurement technology in the literature [5] Virtual metrology [4] Optical emission spectroscopy (OES) Mass spectrometry Plasma impedance monitoring Etc. Outlook-challenges Slide 9 Fig9. OES. [4]

10 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. [6] 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. [7]

11 The Plasma Etching Chamber Slide 11 Fig11-2. RIE Process Chamber. [8] Fig11-1: Typical parallel-plate RIE system. [*] Fig11-3: Typical RF sputtering system. [*] Fig11-4. Physical etch process chamber. [8] * MEMSnet®, https://www.memsnet.org/mems/beginner/etch.html

12 Controls in the Literature Slide 12 Fig12. Etch tool control possibilities with information flow. [4]

13 Controls in the Literature Slide 13 Run-to-Run (R2R) Control [a],[b],[c]. Predictive functional control [4]. 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.

14 Data Outlook-Opportunities Slide 14 Fig10. Endpoint mono-chromtor output over four preventative maintenance (PM) cycles. [4]

15 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

16 Dynamic Virtual Metrology in Semiconductor Manufacturing Outlook-Opportunities Slide 16 Slide from Dr.Chen’s Lam Research Talk

17 Thank you! Q&A Slide 17


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