AlN Sputtering Parameter Estimation Using A Multichannel Parallel DCT Neural Network

In this paper, we present a method for estimating the deposition parameters of the thin film material Aluminum Nitride (AIN) using a deep neural network. The neural network predicts the AIN orientations, which are critical for micromachining Micro-Electro-Mechanical Systems (MEMS) transducers such as accelerometers and acoustic emission sensors. The network features three parallel channels, each equipped with a Discrete Cosine Transform (DCT) based layer that encodes the input parameters into a latent space. This DCT layer applies a hard-thresholding nonlinearity to eliminate noise from the input parameters, resulting in a sparse representation of the latent space. Trained with a dataset comprising AlN orientations parameters and their optimal values, our model is adept at simultaneously extracting and integrating various essential frequency components. Experimental results underscore the effectiveness of our proposed approach in achieving accurate and comprehensive estimation of AlN orientations and MEMS design parameters, thereby providing a promising path for advanced optimization.