BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20211208T164412Z
LOCATION:Level 2 - Exhibit Hall
DTSTART;TZID=America/Los_Angeles:20211208T170000
DTEND;TZID=America/Los_Angeles:20211208T180000
UID:dac_DAC 2021_sess251_DESPOST136@linklings.com
SUMMARY:Machine learning Assisted Design Rule Debug and Rule Ranking Autom
 ation
DESCRIPTION:Designer, IP and Embedded Systems Track Poster Networking Rece
 ption\n\nMachine learning Assisted Design Rule Debug and Rule Ranking Auto
 mation\n\nFu\n\nDesign Technology Co-optimization (DTCO) is the key driver
  for advanced node process development. Design rules change request design
  exploration are evaluated by the process development lithography team. Th
 is paper developed a Machine learning assisted design rule debug system to
  help technologist at the design rule definition phase to balance the desi
 gn request and litho Optical proximity correction(OPC) complexity. Among t
 he layout pattern clips (~100K layout patterns in the case study), a few w
 ill fail after OPC at litho simulation. Design rule definition team need t
 o debug the violations to develop either a new OPC recipe or a new set of 
 rule candidates to remove the specific violations. We have demonstrated a 
 full Machine learning capability and applied in an advanced node process d
 esign rule definition. The paper will cover details:\n1) extremely unbalan
 ced dataset from lithography simulation\n2) Machine learning model selecti
 on and training\n3) Design rule extraction from Machine learning model\n4)
  An random forest based rule generation and rule ranking to produce rule c
 andidates\n\nThe paper further elaborate future direction to drive to symb
 olic learning to give a reasoning layer from general machine learning mode
 l to symbolic rule definition.\n\nVirtual Program: Presented In-Person
END:VEVENT
END:VCALENDAR