Equipment and wafer modeling of batch furnaces by neural networks

In semiconductor manufacturing there is a great demand for innovations towards higher cost-effectiveness. The increasing employment of advanced control systems for process and equipment control is one means to improve manufacturing processes effectively and, hence, to lower costs. A precondition for an accurate and fast control is the availability of process models. In this paper neural networks are applied to non-linear system identification as an alternative or addition to physical models. Neural empirical models are developed with the help of measured input and output data of a system or process. After a brief summary of the theory of neural networks their application to system identification is described in detail. The capabilities of the neural network models are demonstrated by several examples. The temperature dynamics of a vertical furnace for the oxidation of 300 mm wafers as well as the zone temperatures of a 150 mm LPCVD furnace are simulated and the results are verified by measurements. Moreover, in order to control wafer temperatures in batch furnaces, an appropriate model was developed and implemented in a model-based controller.