No abstract

Optical emission spectroscopy (OES) has been widely applied to plasma etching, material processing, development of plasma equipment and technology, as well as plasma propulsion. The collisional-radiative model used in OES is affected by the deviation of fundamental data such as collision cross sections, thus leading to the error in diagnostic results. In this work, a novel method is developed based on feedforward neural network for OES. By comparing the error characteristics of the new method with those of the traditional least-square diagnostic method, it is found that the neural network diagnosis method can reduce the transmission of basic data deviation to the diagnosis results by identifying the characteristics of the spectral vector. This is confirmed by the experimental results. Finally, the mechanism of the neural network algorithm against fundamental data deviation is analyzed. This method also has a good application prospect in plasma parameter online monitoring, imaging monitoring and mass data processing.

Miniaturized ion thrusters are one of the most important candidates in the task of drag-free control for space-based gravitational wave detection, whose thrust can be accurately tuned in principle by in-orbit monitoring and feedback controlling. This work investigates a neural network model (NNM) which can be used for real-time monitoring of the function relating the grid voltage and the extraction current of a miniaturized ion thruster by optical emission spectroscopy. This model is developed as a component of an ion thruster’s digital twin. A collisional-radiative model relates the plasma parameters in the discharge chamber of the thruster to the emission spectroscopy; an extraction current model relates the plasma parameters to the function relating grid voltage and extraction current. The neural network model is trained based on the dataset produced by these models, and is examined by experimental results from a miniaturized ion thruster. It is found that the difference between the thrust predicted by the NNM and the experimental value is less than 6%. Discussions are given on further improvement of the NNM for accurate thrust control in space-based gravitational wave detection in the future.

Space-borne gravitational wave (GW) detection spacecraft works in the state of drag free for which a disturbance reduction system is utilized to offset the non-conservative force. The key actuator of drag-free control loop is a micro-thruster with the performance of a high precision thrust and wide-range operations. Ion thruster, such as electron cyclotron resonance ion thruster (ECRIT), is one of the options because its thrust can be controlled precisely by the method of beam current feedback. However, there is still a barrier for the conventional ECRIT to achieve the low thrust of 1 µN as required in space-borne GWs detection missions due to its radial structure. In this work, a minimized ECR ion thruster (mini-ECRIT) is designed by a new idea that it employs an axial ring-cusped field to exploit the low-pressure adaptability of resonance heating. The mini-ECRIT is tested and results in a dynamic thrust range of 1‒100 µN, a resolution of 0.1 µN, a thrust noise of 0.1 µN/Hz1/2, and a response time of about 10 ms. In addition, the specific impulse of this thruster can reach as high as 510 s at low thrust 5 µN, being higher than that of previous ion thrusters by a factor of about 5, which may significantly increase the lifetime of the system. This minimized ECR ion thruster may support China’s space-borne gravitational wave detection missions such as TianQin.