Wenjian Yu

Stretchable and highly sensitive graphene-on-polymer strain sensors

The use of nanomaterials for strain sensors has attracted attention due to their unique electromechanical properties. However, nanomaterials have yet to overcome many technological obstacles and thus are not yet the preferred material for strain sensors. In this work, we investigated graphene woven fabrics (GWFs) for strain sensing. Different than graphene films, GWFs undergo significant changes in their polycrystalline structures along with high-density crack formation and propagation mechanically deformed. The electrical resistance of GWFs increases exponentially with tensile strain with gauge factors of ~103 under 2~6% strains and ~106 under higher strains that are the highest thus far reported, due to its woven mesh configuration and fracture behavior, making it an ideal structure for sensing tensile deformation by changes in strain. The main mechanism is investigated, resulting in a theoretical model that predicts very well the observed behavior.

Fast capacitance extraction of actual 3-D VLSI interconnects using quasi-multiple medium accelerated BEM

A quasi-multiple medium (QMM) method based on the direct boundary element method (BEM) is presented to extract the capacitance of three-dimensional (3-D) very large scale integration interconnects with multiple dielectrics. QMM decomposes each dielectric layer into a few fictitious medium blocks, and generates an overall coefficient matrix with high sparsity. With the storage technique of a sparse blocked matrix and iterative equation solver generalized minimal residual, the QMM can greatly reduce the CPU time and memory usage of large-scale direct BEM computation. Numerical examples of 3-D multilayered and multiconductor structures cut from actual layout show the efficiency of the QMM method for capacitance extraction. We also compared the QMM accelerated BEM with geometry independent measured equation of invariance (GIMEI) and Zhus overlapping domain decomposition method (ODDM).

Ultra-sensitive graphene strain sensor for sound signal acquisition and recognition

A wearable and high-precision sensor for sound signal acquisition and recognition was fabricated from thin films of specially designed graphene woven fabrics (GWFs). Upon being stretched, a high density of random cracks appears in the network, which decreases the current pathways, thereby increasing the resistance. Therefore, the film could act as a strain sensor on the human throat in order to measure one’s speech through muscle movement, regardless of whether or not a sound is produced. The ultra-high sensitivity allows for the realization of rapid and low-frequency speech sampling by extracting the signature characteristics of sound waves. In this study, representative signals of 26 English letters, typical Chinese characters and tones, and even phrases and sentences were tested, revealing obvious and characteristic changes in resistance. Furthermore, resistance changes of the graphene sensor responded perfectly with pre-recorded sounds. By combining artificial intelligence with digital signal processing, we expect that, in the future, this graphene sensor will be able to successfully negotiate complex acoustic systems and large quantities of audio data.

Enhanced QMM-BEM solver for three-dimensional multiple-dielectric capacitance extraction within the finite domain

The computational time and memory of three-dimensional capacitance extraction have been greatly reduced by using a quasi-multiple medium (QMM) technology, because it enlarges the matrix sparsity produced by the direct boundary element method. In this paper, an approach to automatically determining the QMM cutting pair number and a preconditioning technique are proposed to enhance the QMM-based capacitance extraction. With these two enhancements, the capacitance extraction can achieve much higher speed and adaptability. Experimental results examine the efficiency of two enhancements and show over 10/spl times/ speed-up and memory saving over the multipole approach with comparable accuracy.

Efficient and accurate eye diagram prediction for high speed signaling

This paper introduces an accumulative prediction method to predict the eye diagram for high speed signaling systems. We use the step responses of pull-up and pull-down to extract the worst-case eye diagram, including the eye height and jitter. Furthermore, the method produces the input patterns of the worst-case intersymbol interference. The algorithm handles signals of either symmetric or asymmetric rise/fall time. Experimental results demonstrate the accuracy and efficiency of the proposed method.

Efficient 3-D extraction of interconnect capacitance considering floating metal fills with boundary element method

Inserting dummy (area fill) metals is necessary to reduce the pattern-dependent variation of dielectric thickness in the chemical-mechanical polishing (CMP) process. Such floating dummy metals affect interconnect capacitance and, therefore, signal delay and crosstalk significantly. To take the floating dummies into account, an efficient method for three-dimensional (3-D) capacitance extraction based on boundary element method is proposed. By introducing a floating condition into the direct boundary integral equation (BIE) and adopting an efficient preconditioning technique, and the quasi-multiple medium (QMM) acceleration, the method achieves very high computational speed. For some typical structures of area fill, the presented algorithm has shown over 1000/spl times/ speedup over the industry-standard Raphael while preserving high accuracy. Compared with the recently proposed PASCAL in the work of Park et al. (2000), the proposed method also has about ten times speedup. Since the dummies are not regarded as normal electrodes in capacitance extraction, the proposed method is much more efficient than the conventional method, especially in cases with a large number of floating dummies.

RWCap: A Floating Random Walk Solver for 3-D Capacitance Extraction of Very-Large-Scale Integration Interconnects

A floating random walk (FRW) solver, called RWCap, is presented for the capacitance extraction of very-large-scale integration (VLSI) interconnects. An approach, including the numerical characterization of the cross-interface transition probability and weight value, is proposed to accelerate the extraction of structures with multiple dielectric layers. A comprehensive variance reduction scheme based on the importance sampling and stratified sampling is proposed to improve the convergence rate of the FRW algorithm. Finally, the space management technique using an octree data structure and the parallel computing technique are presented to further improve the efficiency. Numerical experiments are carried out with the test cases generated under the 180 and 45-nm process technologies. They demonstrate that the proposed multidielectric FRW algorithm achieves up to 160× speedup over the FRW algorithm using spherical transition domains to cross dielectric interface, with very small memory overhead. The variance reduction techniques further bring 3× or more speedup without memory overhead and the loss of accuracy. The RWCap also outperforms other existing FRW algorithm and fast boundary element method solvers in terms of computational time or scalability. The experiments on an 8-core CPU machine show that the parallel RWCap is over 6× faster than its serial-computing version.

Hierarchical block boundary-element method (HBBEM): a fast field solver for 3-D capacitance extraction

As feature size decrease, fast and accurate parasitic capacitance extraction has become increasingly critical for verification and analysis in very large scale integration design. In this paper, a fast hierarchical-block boundary-element method based on the boundary-element method (BEM) is presented for three-dimensional (3-D) capacitance extraction, which can give out the global capacitance matrix directly. It assigns the global computation of 3-D domain into local computation in BEM blocks by hierarchical partition 3-D structure. The boundary capacitance matrix (BCM) is computed in the BEM block using all the known conditions. Reuse technology can decrease the running time. After merging the BCMs of all BEM blocks, the global capacitance matrix for a given set of conductors can be computed. Numerical results show that this global hierarchical approach can get very high speed in 3-D computation with equal accuracy as the 3-D field solver.

Efficient Space Management Techniques for Large-Scale Interconnect Capacitance Extraction With Floating Random Walks

In the capacitance extraction with the floating random walk (FRW) algorithm, the space management approach is required to facilitate finding the nearest conductor. The Octree and grid-based spatial structures have been used to decompose the whole domain into cells and to store information of local conductors. In this letter, the techniques with the distance limit of cell and only searching in cells neighbor region are proposed to accelerate the construction of the spatial structures. A fast inquiry technique is proposed to fasten the nearest conductor query. We also propose a grid-Octree hybrid structure, which has advantages over existing structures. Experiments on large very large scale integration structures with up to 484441 conductors have validated the efficiency of the proposed techniques. The improved FRW algorithm is faster than RWCap for thousands times while extracting a single net, and several to tens times while extracting 100 nets.

An efficient method for chip-level statistical capacitance extraction considering process variations with spatial correlation

An efficient method is proposed to consider the process variations with spatial correlation, for chip-level capacitance extraction based on the window technique. In each window, an efficient technique of Hermite polynomial collocation (HPC) is presented to extract the statistical capacitance. The capacitance covariances between windows are then calculated to reflect the spatial correlation. The proposed method is practical for chip-level extraction task, and the experiments on full-path extraction exhibit its high accuracy and efficiency.