Enhanced Temperature Control Method Using ANFIS with FPGA

Abstract

Temperature control in etching process is important for semiconductor manufacturing technology. However, pressure variations in vacuum chamber results in a change in temperature, worsening the accuracy of the temperature of the wafer and the speed and quality of the etching process. This work develops an adaptive network-based fuzzy inference system (ANFIS) using a field-programmable gate array (FPGA) to improve the effectiveness. The proposed method adjusts every membership function to keep the temperature in the chamber stable. The improvement of the proposed algorithm is confirmed using a medium vacuum (MV) inductively-coupled plasma- (ICP-) type etcher.

Novel Nonlinear Control Method for First‐Order Systems and Its Implementation in a Temperature System

“…x T S 􏼁 � (1 − P%)r +(P%)x 0 ; for a constant reference r, (21) where ( 20) and ( 21) indicate how close x(t) is to the reference at time T S , and selecting an adequate percentage %P, the time T S can be modifed supposing diferent values of k (19).…”

“…Te gain k is useful for designing controllers that speed up or slow down the convergence rate to the desired reference; this means that k must guarantee that the percentage convergence given in (22) is fulflled. In order to fnd k, we use the equation (19), obtaining a m � −1/T S ln (P%) � a − k, and therefore,…”

“…In addition, a system was developed that helps to determine the membership function with optimisation for the fastest processing of solving problems. In [19], an adaptive network-based fuzzy inference system was developed using a feld programmable gate array to improve the efectiveness and the results of temperature changes in the etching process. A self-tuning proportional-integral-derivative control was proposed in [20] to address the matter of controlling temperatures for drawn-texturing yarn machines.…”

Quick Response Circulating Water Cooling of ±3 mK Using Dynamic Thermal Filtering

“…A control algorithm based on a Fuzzy-PID was employed to obtain better performance of CCW temperature control, since the Fuzzy-PID can deal with nonlinearity, time variety, and large time delays of the CCW temperature control system [19], as shown in Figure 9.…”

“…Therefore, online PID parameter regulation was achieved by the fuzzy controller to gain better performance of CCW temperature control. A control algorithm based on a Fuzzy-PID was employed to obtain better performance of CCW temperature control, since the Fuzzy-PID can deal with nonlinearity, time variety, and large time delays of the CCW temperature control system [19], as shown in Figure 9. Therefore, online PID parameter regulation was achieved by the fuzzy controller to gain better performance of CCW temperature control.…”