In this paper, we present a novel broadband radio frequency (RF) sensor technology, which can be used for plasma process control, including Fault Detection and Classification (FDC). Plasma is a non-linear complex electrical load, therefore generates harmonics of the driving frequency in the electrical circuit. Plasma etch processes have dependencies on chamber pressure, delivered power, wall and substrate temperatures, gas phase and surface chemistry, chamber geometry and particles, and many other second order contributions. Any changes, which affect the plasma complex impedance, will be reflected in the Fourier spectrum of the driving RF power source.
We have found that high-resolution broadband sensing, up to 1GHz or more than 50 harmonics (for a fundamental frequency of 13.56MHz), greatly increases the effectiveness of RF sensing for process-state monitoring. This paper describes the measurement sampling technique; the broadband RF sensor and presents data from commercial plasma etch tool monitoring.
Knowledge-based process control integrates advanced sensors with tool and process models for enhanced fault detection and classification (FDC) performance. Rather than use a statistical or template-based control model, the knowledge-based approach is constructed around core information extracted from the process itself. The approach uses data from an advanced sensor that is known to be tool and process sensitive. In this way, the process itself does much of the data compression, rather than having to rely on statistical algorithms compiled from the tool inputs. Because it works with a knowledge of the tool itself, built through observations of the sensor data as systematic changes are made to tool and process conditions, data is used to construct a fault library upon which the FDC engine is based. A fundamental tool/process health indicator reports any excursions that match those in the library. The fault is detected and classified in real time.