Machine learning methods applied to process qualification

With the substantial surge in the need for high-end masks it becomes increasingly important to raise the capacity of the corresponding production lines. To this end the efficient qualification of matching tools and processes within a production line is of utmost relevance. Matching is typically judged by the processing of dedicated lots on the new tool and process. The amount of qualification lots should on the one hand be very small, as the production of qualification plates is expensive and uses capacity of the production corridor. On the other hand the strict requirements of high-end products induce very tight specification limits on the matching criteria. It is thus often very difficult to assess tool or process matching on the basis of a small amount of lots. In this paper we expound on a machine learning based strategy which assesses the mask characteristics of a qualification plate by learning the typical behavior of these characteristics within the production line variations. We show that by careful selection of reference production plates as well as by setting specification limits based on the production behavior we can manage the qualification tasks efficiently by using a small number of masks. The specification characteristics as well as the specific limits are selected and determined using a Naive Bayes learner. The resulting performance for prediction of tool and process matching is assessed by considering the resulting receiving operator curve. As a result we obtain an approach towards the assessment of qualification data which enables engineers to assess the tool and process matching using a small amount of matching data under the constraint of substantial measurement uncertainties. As an outlook we discuss how this approach can be used to examine the reverse question of detecting process failures, i.e. the automated ability to raise a flag when the current production characteristics start to deviate from their typical characteristics. Overall, in this paper we show how the rapidly evolving field of machine learning increasingly impacts the semiconductor production process.