Production-worthy full chip image-based verification

Abstract

At 65nm technology node and below, with the ever-smaller process window, it is no longer sufficient to apply traditional model-based verification at only the nominal condition. Full-chip, full process-window verification has started to integrate into the OPC flow at the 65nm production as a way of preventing potentially weak post-OPC designs from reaching the mask making step. Through process-window analysis can be done by way of simulating wafer images at each of the corresponding focus and exposure dose conditions throughout the process window using an accurate and predictive FEM model. Alternatively, due to the strong correlation between the post-OPC design sensitivity to dose variation and aerial image (AI) quality, the study of through-dose behavior of the post-OPC design can also be carried out by carefully analyzing the AI. These types of analysis can be performed at multiple defocus conditions to assess the robustness of the post-OPC designs with respect to focus and dose variations. In this paper, we study the AI based approach for post-OPC verification in detail. For metal layer, the primary metrics for verification are bridging, necking, and via coverage. In this paper we are mainly interested in studying bridging and necking. The minimum AI value in the open space gives an indication of its susceptibility to bridging in an over-dosed situation. Lower minimum intensity indicates less risk of bridging. Conversely, the maximum AI between the metal lines provides indication of potential necking issues in an under-dosed situation. At times, however, in a complex 2D pattern area, the location as to where the AI reaches either maximum or minimum is not obvious. This requires a full-chip, dense image-based approach to fully explore the AI profile of the entire space of the design. We have developed such an algorithm to find the AI maximums and minimums that will bear true relevance to the bridging and necking analysis. In this paper, we apply the full-chip image-based analysis to 65nm metal layers. We demonstrate the capturing of potential bridging or necking issues as identified by the AI analysis. Finally, we show the performance of the full-chip image-based verification.