Intelligent Optimization of Dosing Uniformity in Ion Implantation Systems

Spatial dose non-uniformity is a key variation of concern in ion implantation. These non-uniformities are often compensated for by adjusting the implantation times spent at each point on the wafer: areas with a low uncompensated dose are assigned more implantation time, while those with a greater dose are assigned reduced times. In this paper, we present a machine learning based method that rapidly learns a set of compensating implantation times in order to achieve a desired uniformity.We propose an iterative tuning approach comprised of two components. The first component is an empirical Bayesian forward model that infers the relationship between the implantation times and the implantation dose profile. The model is updated as new implantations are performed and measured, enabling progressive accuracy improvements during tuning. The second component is an optimization method that selects compensating times by solving a constrained optimization problem. When tuning a process, we alternate between these two components, repeatedly selecting compensating times, measuring the resulting dose profile and updating the model until the desired uniformity is achieved. Our proposed method is compared to the conventional non-Bayesian industry method of record, and converges to a desired uniformity in significantly fewer iterations. Finally, the solutions found by our method result in a greater total dose given the same total implantation time and uniformity, increasing the throughput of implantation with no added cost.