• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.1
• /
• pp.603-608
• /
• 2018

Although the voltage-applied discharge method is most widely used in the semiconductor and display industries, periodic management costs are incurred because the method causes defects due to the absorption of ambient fine dust and causes emitter tip contamination due to the discharge. The emitter tip contamination problem is caused by the accumulation of fine particles in ambient air due to the corona discharge of the ionizer. Fuzzy ball generation accelerates the wear of the emitter tip and deteriorates the performance of the ionizer. Although a mechanical cleaning method using a manual brush or an automatic brush is effective for contaminant removal, it requires management of additional mechanical parts by the user. In some cases, contaminants accumulated in the emitter may be transferred to the wafer or product. In order to solve this problem, we developed an ionizer for a clean environment that can remove the pencil-type emitter tip and directly ionize the surrounding gas molecules using the tungsten wire located inside the ion tank. As a result of testing and certification by the Korea Institute of Machinery and Materials, the average concentration was $0.7572particles/ft^3$, the decay time was less than two seconds, and the ion valance was 7.6 V, which is satisfactory.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.243-246
• /
• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.