Shupeng Sun, Fa Wang, et al.
IEEE TCAS-I
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.
Shupeng Sun, Fa Wang, et al.
IEEE TCAS-I
Ganesh Venkataraman, Jiang Hu, et al.
DATE 2006
Mohamed Baker Alawieh, Fa Wang, et al.
ISQED 2016
Ganesh Venkataraman, Jiang Hu, et al.
IEEE Transactions on VLSI Systems