2012 IEEE International Test Conference 2012
DOI: 10.1109/test.2012.6401546
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Systematic defect screening in controlled experiments using volume diagnosis

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Cited by 6 publications
(3 citation statements)
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“…Specifically, certain layout features are more difficult to manufacture than others and are more likely to lead to circuit failures. When such features are present multiple times in a chip, they can result in repeated or systematic defects, which can impact the yield and defective-parts-per-million (DPPM) significantly [5,17,18,32,33,38]. Due to modeling errors and algorithmic inaccuracies in removing the resulting systematic variations, process-related corrective actions using OPC/RET techniques are not sufficient for acceptable yield and DPPM [3].…”
Section: Introductionmentioning
confidence: 99%
“…Specifically, certain layout features are more difficult to manufacture than others and are more likely to lead to circuit failures. When such features are present multiple times in a chip, they can result in repeated or systematic defects, which can impact the yield and defective-parts-per-million (DPPM) significantly [5,17,18,32,33,38]. Due to modeling errors and algorithmic inaccuracies in removing the resulting systematic variations, process-related corrective actions using OPC/RET techniques are not sufficient for acceptable yield and DPPM [3].…”
Section: Introductionmentioning
confidence: 99%
“…Volume diagnosis based analysis [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62][63][64][65][66] can be applied to different stages of yield ramp-up and serves different purposes, such as identification and quantification of an existing critical feature [37-42, 47, 48], identification of an unknown systematic feature [43], [51], [62], validation and calibration of DFM rules [43], [63], defect density and distribution estimation for a random defect [64], and yield monitoring [40], [61]. In this work, these approaches are all referred as diagnosis driven yield analysis (DDYA).…”
Section: Previous Workmentioning
confidence: 99%
“…The root cause information of defects thus obtained can be categorized as following: 1. Feature failure rate (FFR) estimation [37][38][39][40][41][42][43][44][45][46]; 2. Feature/root cause probability [47], [48]; 3.…”
Section: Previous Workmentioning
confidence: 99%