New applications on multi-beam mask writers to enable mask-making in 3nm and beyond

To meet the demanding requirements for pattern fidelity, critical dimension and placement errors on advanced masks, the use of multi-beam mask writers together with using low sensitivity resists became necessary and inevitable. To reach the targeted throughput on such low sensitivity resists, an increase of the beam current is necessary which results in two problems. Worse beam stability control increases the risks of pattern errors and thus leads to higher yield loss. In addition, stronger resist charging and thermal effects also result in more unpredictable displacement errors which in turn make overlay control much more difficult. Here we present a new method that utilizes machine learning to detect tool abnormalities and trigger immediate exposure interruption which significantly reduces the mask yield loss. To reduce and compensate stronger charging and thermal effects from a higher beam current, we introduce hardware modifications and software corrections as well as an exposure sequence optimization that in combination minimize the yield loss and overlay problems and enable mask making in 3nm and beyond.