Development of Artificial Neural Network and Topology Reconstruction Schemes for Fan-Out Wafer Warpage Analysis

How to accurate predict the topology of a constituted wafer and its warpage would be critical in improving processing reliabilities. Traditionally, Stoney equation has been widely used to correlate film stress and wafer warpage. However, it only works under ideal situation and could be deviated from real situation significantly. Many previous studies thus have been performed to revise the relation but these analytical-based formulations usually take single factor into consideration. In reality, multiple imperfection issues are usually simultaneously existed and pure analytical approach would be too challenging to yield useful results. Instead, data-driven methods such as artificial neural network might be feasible to achieve effective black box mapping to evaluate the problem. Specifically, the stress state of bi-layer structures with thicker, viscoelastic, and multi-layer films are investigated in this work to demonstrate the feasibility. The multilayer perception model is chosen and the effects of thick film, viscoelasticity, and multiple layers on film stress are individually investigated subsequently. Finally, all three factors are simultaneously considered under the same MLP structure and a 99% successful rate can be achieved based on a 5% deviation threshold with 2300 simulation data. Meanwhile, a program is designed to reconstruct and visualize the deformed wafer surface from local curvatures as the preparation for final real 3D reconstitute structure study in the future.