In this project, we propose to develop machine learning-based solutions for attesting the provenance of manufactured wafers containing integrated circuits (ICs), in order to provide assurance regarding their origin and curtail the risk of using these ICs in sensitive applications. Our conjecture is that the foundry, the equipment, and the mask-set used during manufacturing leave a systematic signature on fabricated wafers, which can be measured, modeled and compared against for the purpose of provenance attestation. Accordingly, we propose to expand existing statistical side-channel fingerprinting methods and to develop novel machine learning-based methods which will use measurements obtained at various steps of semiconductor manufacturing to quantitatively assess the likelihood that a wafer was fabricated at a trusted facility, through a trusted line of equipment and with a trusted mask-set. To evaluate our method, we will initially use an extensive dataset that has been furnished to us from one of the IAB members (GlobalFoundries), containing all available relevant data from approximately 10K wafers fabricated in a 12nm FinFET technology. Additional data and synthetic enhancement thereof will also be pursued throughout this project to broaden the evaluation scope of the developed methods.