2013
DOI: 10.1117/12.2011594
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High-volume process monitoring of FEOL 22nm FinFET structures using an automated STEM

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Cited by 11 publications
(6 citation statements)
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“…Compared with traditional top‐down thinning techniques, the technique of inverted thinning is also a method to effectively mitigate curtaining, which changes the direction of ion beam polishing so that curtains do not appear at the area of interest on the lamella. [ 77–80 ] In this case, the metal contacts of the transistor are at the bottom, so the curtain from the contacts does not affect the structure of the transistor (usually a key feature of interest). However, the curtain effect in the contact of the metal layer still exists.…”
Section: Improved Fib–sem Sample Preparation Methods For Special Devicesmentioning
confidence: 99%
“…Compared with traditional top‐down thinning techniques, the technique of inverted thinning is also a method to effectively mitigate curtaining, which changes the direction of ion beam polishing so that curtains do not appear at the area of interest on the lamella. [ 77–80 ] In this case, the metal contacts of the transistor are at the bottom, so the curtain from the contacts does not affect the structure of the transistor (usually a key feature of interest). However, the curtain effect in the contact of the metal layer still exists.…”
Section: Improved Fib–sem Sample Preparation Methods For Special Devicesmentioning
confidence: 99%
“…Geologists and engineers in the oil and gas industry are very interested in the fine microscopic structure of containing the target from the wafer, transfer the thick section to a TEM grid, and thin the section to the required final thickness, including a final polishing step at low beam energy to remove surface damage created earlier in the process (Figure 2) [3]. In addition to extensive developments in automation, perfecting this capability required sophisticated improvements in the electron and ion columns.…”
Section: Tem Sample Preparationmentioning
confidence: 99%
“…In this study, we present (S)TEM metrology characterization [5][6][7][8][9] using an automated Thermo Scientific TM Metrios TM (S)TEM on two typical process challenges. We introduce an internal machine learning-based modeling algorithm to address the challenge of measuring GAA structures with sacrificial SiGe etch process variations using a forksheet device.…”
Section: Introductionmentioning
confidence: 99%