Most virtual reality (VR) applications use a commercial controller for interaction. However, a typical virtual reality controller (VRC) lacks positional precision and accuracy in millimeter-scale scenarios. This lack of precision and accuracy is caused by built-in sensors drift. Therefore, the tracking performance of a VRC needs to be enhanced for millimeter-scale scenarios. Herein, we introduce a novel way of enhancing the tracking performance of a commercial VRC in a millimeter-scale environment using a deep learning (DL) algorithm. Specifically, we use an attention model trained with data collected from a linear motor, an IMU sensor, and a VRC. We integrate the virtual environment developed in Unity software with the attention model running in Python.