Xiaoji Ye, Frank Liu, et al.
IEEE Transactions on VLSI Systems
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.
Xiaoji Ye, Frank Liu, et al.
IEEE Transactions on VLSI Systems
Frank Liu, Peter Feldmann
ISCAS 2011
Cheng-Wei Yu, Frank Liu, et al.
Hydrology and Earth System Sciences
Wangyang Zhang, Karthik Balakrishnan, et al.
ICICDT 2012