Prediction of critical dimensions for 3D TSV structures using artificial neural network

TSV (Through-Silicon Via) is a vertical interconnection structure achieved by creating holes in the silicon substrate of a chip. This paper explores the subtle differences in the reflection characteristics of light across various TSV structures. Electromagnetic simulation software, namely, Lumerical FDTD, based on the Finite-Difference Time-Domain (FDTD) method, was employed to establish electromagnetic models for TSV structures. 3D-FDTD simulations calculate near-field electromagnetic field data and derive near-field reflection spectra. The dataset comprises different TSV structures with varying critical dimensions (CD) and their corresponding far-field reflection spectra obtained through simulation. Our approach introduces two deep learning networks: the forward network and the inverse network. Given the time-consuming nature of FDTD electromagnetic simulation tools when dealing with complex structures, the forward network is trained to rapidly predict reflection spectrum signal data corresponding to CD parameters, effectively replacing simulation software. Using a threshold of 1% to determine the accuracy of the spectral predictions, forward prediction achieves 100% accuracy. Similarly, the inverse network is trained to predict the CD parameters of TSV structures from the reflection spectrum signal, allowing for fast and accurate inference of the dimensions of TSV structures and the extraction of geometric parameters. In reverse prediction, the MAPE (Mean Absolute Percentage Error) for R-top, height, and R-bot remains consistently below 5%.