Hotspot detection based on litho-aware machine learning

One or more non-transitory computer-readable media storing computer-executable instructions for causing one or more processors to perform a method, the method comprising:

• receiving a layout design;
• identifying pinching-type hotspot candidates and bridging-type hotspot candidates in the layout design based on predetermined criteria;
• performing simulation to derive aerial image intensity values for a plurality of sites on each of the pinching-type hotspot candidates and for a plurality of sites on each of the bridging-type hotspot candidates;
• determining pinching-type hotspots from the pinching-type hotspot candidates based on one or more machine learning models for pinching-type hotspots and bridging-type hotspots from the bridging-type hotspot candidates based on one or more machine learning models for bridging-type hotspots, the input for the one or more machine learning models for pinching-type hotspots being the aerial image intensity values for the plurality of sites on each of the pinching-type hotspot candidates, the input for the one or more machine learning models for bridging-type hotspots being the aerial image intensity values for the plurality of sites on each of the bridging-type hotspot candidates;
• storing information of the pinching-type hotspots and the bridging-type hotspots;
• and
• finalizing the layout design to repair the pinching-type hotspots and the bridging-type hotspots, wherein a circuit is thereafter manufactured according to the finalized layout design using a photolithographic process.

A system, comprising:

• one or more processors, the one or more processors programmed to perform a method, the method comprising:
• receiving a layout design;
• identifying pinching-type hotspot candidates and bridging-type hotspot candidates in the layout design based on predetermined criteria;
• performing simulation to derive aerial image intensity values for a plurality of sites on each of the pinching-type hotspot candidates and for a plurality of sites on each of the bridging-type hotspot candidates;
• determining pinching-type hotspots from the pinching-type hotspot candidates based on one or more machine learning models for pinching-type hotspots and bridging-type hotspots from the bridging-type hotspot candidates based on one or more machine learning models for bridging-type hotspots, the input for the one or more machine learning models for pinching-type hotspots being the aerial image intensity values for the plurality of sites on each of the pinching-type hotspot candidates, the input for the one or more machine learning models for bridging-type hotspots being the aerial image intensity values for the plurality of sites on each of the bridging-type hotspot candidates;
• storing information of the pinching-type hotspots and the bridging-type hotspots;
• and
• finalizing the layout design to repair the pinching-type hotspots and the bridging-type hotspots, wherein a circuit is thereafter manufactured according to the finalized layout design using a photolithographic process.