Yun Ye, Frank Liu, et al.
DAC 2009
In this paper, we propose a new technique, referred to as virtual probe (VP), to efficiently measure, characterize, and monitor spatially-correlated inter-die and/or 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 silicon characterization. By exploring the underlying sparse pattern in spatial frequency domain, VP achieves substantially lower sampling frequency than the well-known Nyquist rate. In addition, VP is formulated as a linear programming problem and, therefore, can be solved both robustly and efficiently. Our industrial measurement data demonstrate the superior accuracy of VP over several traditional methods, including 2-D interpolation, Kriging prediction, and k-LSE estimation. © 2006 IEEE.
Yun Ye, Frank Liu, et al.
DAC 2009
Ben R. Hodges, Frank Liu
Foundations and Trends in Electronic Design Automation
Frank Liu
IEEE Design and Test
Xiaoji Ye, Frank Liu, et al.
IEEE Transactions on VLSI Systems