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In this paper, we propose a new technique, referred to as Multi-Wafer Virtual Probe (MVP) to efficiently model wafer-level spatial variations for nanoscale integrated circuits. Towards this goal, a novel Bayesian inference is derived to extract a shared model template to explore the wafer-to-wafer correlation information within the same lot. In addition, a robust regression algorithm is proposed to...
The expensive cost of testing and characterizing parametric variations is one of the most critical issues for today's nanoscale manufacturing process. In this paper, we propose a new technique, referred to as Bayesian Virtual Probe (BVP), to efficiently measure, characterize and monitor spatial variations posed by manufacturing uncertainties. In particular, the proposed BVP method borrows the idea...
In this paper, we propose a new technique, referred to as virtual probe (VP), to efficiently measure, characterize and monitor both inter-die and spatially-correlated intra-die variations in nanoscale manufacturing process. VP exploits recent breakthroughs in compressed sensing to accurately predict spatial variations from an exceptionally small set of measurement data, thereby reducing the cost of...
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