Abstract
This study investigated an adaptive control, fault diagnostics and prognostics of the anode voltage regulator system at an ion implantation accelerator. The system was modeled as a 4th order AutoRegressive with eXogenous (ARX) model, controlled by a Fuzzy Logic Controller (FLC). This model was then used as a basis for constructing and updating a fault diagnosis module and a failure prognostics module. To maintain the system’s performance, the controller’s response was continuously re-adjusted through an optimization scheme. A Failure Mode and Effect Analysis (FMEA) was conducted resulting on five failure modes of the regulator system. Fault data were generated in MATLAB simulation to train a random forest fault classification engine. The optimal random forest classifier was 20 decision trees with a fault diagnostics accuracy of 98.06%. A Hidden Markov Model (HMM) was constructed as the system’s fault progression model based on the interaction between environmental conditions and controller actions. The particle filter and Bayesian inference methods were then employed to continuously update the HMM and predict the system’s Remaining Useful Lifetime (RUL). The proposed methodology was able to integrate an adaptive fuzzy logic control, prognosis and failure diagnosis altogether allowing a continual satisfactory performance of the voltage regulator system throughout its lifetime.
Highlights
Ion implantation accelerator is a device commonly used to fabricate semiconductor wafers by accelerating and impacting charged-particles to a target material
This study developed an integrated approach to control the performance of the anode voltage regulator system at an ion implantation accelerator throughout its lifetime despite degradations
The approach relied on the modeling the system empirically through a linear system identification method
Summary
Ion implantation accelerator is a device commonly used to fabricate semiconductor wafers by accelerating and impacting charged-particles to a target material. The implantation process is highly affected by several critical factors, namely the ion beam’s energy and intensity. These two variables are directly regulated by the electrical voltage applied to the accelerator’s anode. When the variation is due to degradations within the system, there exists a limit beyond which the controller cannot sufficiently adapt to maintain the desired performance requirements. It is, a strategic interest to characterize the degradation’s evolution and predict when to perform corrective maintenance to ensure a continuous satisfactory performance of the system
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