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DTSTAMP:20260730T152640Z
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UID:dac_DAC 2026_sess306_WIP3307@linklings.com
SUMMARY:O-RRP: Optical Proximity Correction Runtime and Resource Predictio
 n using Machine Learning
DESCRIPTION:Neha Sharma, Wyatt Clarke, Dallas Lea, Lei Zhuang, Jeonghee Ki
 m, Harsha Krishnareddy, Edward Seabolt, Vandana Mukherjee, and Gi-Joon Nam
  (IBM Research)\n\nGrowth of compute resource demand in Optical Proximity 
 Correction (OPC) is driving the adoption of cloud-based workflows, making 
 accurate prediction of runtime and memory usage crucial to optimize resour
 ce allocation and scheduling. This paper presents a machine learning-based
  method for predicting OPC runtime and memory usage using data from the la
 rge number of worker jobs contained in a single representative OPC run. Re
 gression models are trained on extracted geometrical features to capture t
 he relationship between layout characteristics and compute resource consum
 ption. Experimental results demonstrate total runtime prediction within 11
 .6% and peak memory prediction within 5.3% of actual measurements across m
 ultiple design layouts and recipes. This method can be easily integrated i
 nto existing OPC pipelines, allowing seamless deployment of optimized hybr
 id multi-cloud environments for substantially faster tape-out cycles throu
 gh improved infrastructure utilization.\n\nTrack: Student\n\n
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