Method for machine learning a detection of at least one irregularity in a plasma system, particularly an RF powered plasma processing system, comprising: - Providing at least one input signal each related to an analog signal of a power delivery system for the plasma system and/or to another characteristic of the power delivery system and/or of the plasma system, the at least one input signal having at least one irregularity feature indicative of the irregularity in the plasma system, - Performing a machine learning procedure wherein the at least one input signal having the at least one irregularity feature is processed by a programmable circuit to train the detection of the irregularity in the plasma system; wherein the programmable circuit carries out, at least partially, a detection procedure, particularly comprising an application of a neural network, preferably a pattern recognition or pattern matching using the neural network, or an algorithm, for identifying the at least one irregularity feature, and wherein the machine learning procedure comprises: - Performing, by the circuit , the processing of the at least one input signal using the detection procedure and at least one configurable parameter of the detection procedure, particularly weights of the neural network or parameters of the algorithm, wherein a configuration of the at least one parameter is varied, particularly modified, for each processing of the input signal to obtain respective processing results , - Determining at least one parameter result, particularly comprising a selection of the varied configurations, as a training result of the machine learning based on the processing results ; characterized in that the detection procedure for the detection of the at least one irregularity is implemented in the programmable circuit by combining functional blocks of the circuit for carrying out calculations in real time, wherein the implemented detection procedure is used iteratively with real-time calculations during the machine learning procedure for the processing of the at least one input signal and wherein the implemented detection procedure is used for the training of the plasma system by repeatedly processing pre-existing training signals to determine an optimum configuration for a real-time processing mode. - Providing at least one input signal each related to an analog signal of a power delivery system for the plasma system and/or to another characteristic of the power delivery system and/or of the plasma system, the at least one input signal having at least one irregularity feature indicative of the irregularity in the plasma system, - Performing a machine learning procedure wherein the at least one input signal having the at least one irregularity feature is processed by a programmable circuit to train the detection of the irregularity in the plasma system; wherein the programmable circuit carries out, at least partially, a detection procedure, particularly comprising an application of a neural network, preferably a pattern recognition or pattern matching using the neural network, or an algorithm, for identifying the at least one irregularity feature, and wherein the machine learning procedure comprises: - Performing, by the circuit , the processing of the at least one input signal using the detection procedure and at least one configurable parameter of the detection procedure, particularly weights of the neural network or parameters of the algorithm, wherein a configuration of the at least one parameter is varied, particularly modified, for each processing of the input signal to obtain respective processing results , - Determining at least one parameter result, particularly comprising a selection of the varied configurations, as a training result of the machine learning based on the processing results ; <EOS>