Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

Detecting layout hotspots is one of the key problems in physical verification flow. Although
machine learning solutions show benefits over lithography simulation and pattern matching …

In the last five decades, the number of transistors on a chip has increased exponentially in
accordance with the Moore's law, and the semiconductor industry has followed this law as …

With the advancement of very large scale integrated circuits (VLSI) technology nodes,
lithographic hotspots become a serious problem that affects manufacture yield. Lithography …

With the continuous shrinking of technology nodes, lithography hotspot detection and
elimination in the physical verification phase is of great value. Recently machine learning …

As the physical design of semiconductors continues to shrink, the lithography process is
becoming more sensitive to layout design. Identifying lithography hotspots (HSs) in the …

As the circuit feature size continuously shrinks down, hotspot detection has become a more
challenging problem in modern DFM flows. Developed deep learning techniques have …

With the aggressive and amazing scaling of the feature size of semiconductors, hotspot
detection has become a crucial and challenging problem in the generation of optimized …

As VLSI technology nodes continue, the gap between lithography system manufacturing
ability and transistor feature size induces serious problems, thus lithography hotspot …

Lithography simulation is computationally expensive for hotspot detection. Machine learning
based hotspot detection is a promising technique to reduce the simulation overhead …