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BEGIN:VEVENT
DTSTAMP:20260402T024533Z
LOCATION:Engineering Posters\, Level 2 Exhibit Hall
DTSTART;TZID=America/Los_Angeles:20250624T170000
DTEND;TZID=America/Los_Angeles:20250624T180000
UID:dac_DAC 2025_sess264_ENGPOST101@linklings.com
SUMMARY:CTS with Machine Learning NDR
DESCRIPTION:Sungsu Byun (Samsung)\n\nCTS (Clock Tree Synthesis) is importa
 nt to optimize design. It is necessary of CTS as Design Methodology to get
  robust clock tree in terms of latency, skew, physical track, clock tree d
 epth and clock power. There are many ways to synthesis and optimize by cha
 nging the type of clock cells or by locating the clock cells with fixed ND
 R rules for BEOL. (NDR is non default rule for clock net routing which def
 ined routing width, spacing and shielding rule). But there were a few stud
 ies for NDR.\n It is necessary to get optimal NDR with Machine learning in
  advance from CTS by apply differential NDR for each nets based on Machine
  Learning. \n By using various delay depending on different clock width, C
 TS can be optimized more. However, Since it is hard to change or choose th
 e different NDR on every clock nets, Machine learning algorithm is necessa
 ry to get more better optimal clock tree. \nThe result of gain is high wit
 hout any side effects.\n\nTopics: Front-End Design\n\n
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