Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF). Our key idea is to borrow the simulation data generated from an early stage (e.g., schematic level) to facilitate efficient high-dimensional performance modeling at a late stage (e.g., post layout) with low computational cost. Such a goal is achieved by statistically modeling the performance correlation between early and late stages through Bayesian inference. Several circuit examples designed in a commercial 32nm CMOS process demonstrate that BMF achieves up to 9× runtime speedup over the traditional modeling technique without surrendering any accuracy.
In this paper, we propose a new technique, referred to as MultiWafer 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 automatically detect and remove outliers (i.e., abnormal measurement data with large error) so that they do not bias the modeling results. The proposed MVP method is extensively tested for silicon measurement data collected from 200 wafers at an advanced technology node. Our experimental results demonstrate that MVP offers superior accuracy over other traditional approaches such as VP [7] and EM [8], if a limited number of measurement data are available.
Controlled
droplet manipulation by light has tremendous technological
potential. We report here a method based on photothermally induced
pyroelectric effects that enables manipulation and maneuvering of
a water droplet on a superhydrophobic surface fabricated on lithium
tantalite (LiTaO3). In particular, we demonstrate that
the pyroelectric charge distribution has an essential role in this
process. Evenly distributed charges promote a rapid hydrophobic to
hydrophilic transition featuring a very large water contact angle
(WCA) change of ∼76.5° in air. This process becomes fully
reversible in silicone oil. In contrast, the localized charge distribution
induced by guided laser illumination leads to very different and versatile
functionalities, including droplet shape control and motion manipulation.
The influence of a saline solution is also investigated and compared
to the deionized water droplet. The focusing effect of the water droplet,
a phenomenon that widely exists in nature, is particularly of interest.
Simple tuning of the laser incident angle results in droplet deformation,
jetting, splitting, and guided motion. Potential applications, such
as droplet pinning and transfer, are presented. This approach offers
a wide range of versatile functionalities and ready controllability,
including contactless, electrodeless, and precise spatial and fast
temporal control, with tremendous potential for applications requiring
remote droplet control.
Abstract-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.
Parametric yield estimation is one of the most critical-yetchallenging tasks for designing and verifying nanoscale analog and mixed-signal circuits. In this paper, we propose a novel Bayesian model fusion (BMF) technique for efficient parametric yield estimation. Our key idea is to borrow the simulation data from an early stage (e.g., schematic-level simulation) to efficiently estimate the performance distributions at a late stage (e.g., post-layout simulation). BMF statistically models the correlation between early-stage and late-stage performance distributions by Bayesian inference. In addition, a convex optimization is formulated to solve the unknown late-stage performance distributions both accurately and robustly. Several circuit examples designed in a commercial 32 nm CMOS process demonstrate that the proposed BMF technique achieves up to 3.75× runtime speedup over the traditional kernel estimation method.
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