@inproceedings {Rhodes Island, Greece, title = {Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach}, year = {2023}, month = {06/2023}, address = {Rhodes Island, Greece}, keywords = {psclab}, author = {Zhikang Zhang and Bruno Machado Trindade and Michael Green and Zifan Yu and Christopher Pawlowicz and Fengbo Ren} } @inproceedings {Bordeaux, France, title = {A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images}, year = {2022}, month = {10/2022}, address = {Bordeaux, France}, abstract = {

This paper proposes an automated data-driven integrated
circuit segmentation approach of scan electron microscopy
(SEM) images inspired by state-of-the-art CNN-based image
perception methods. Based on the requirements derived from
real industry applications, we take wire segmentation and via
detection algorithms to generate integrated circuit segmentation
maps from SEMs in our approach. On SEM images
collected in the industrial applications, our method achieves
an average of 50.71 on Electrically Significant Difference
(ESD) in the wire segmentation task and 99.05\% F1 score
in the via detection task, which achieves about 85\% and 8\%
improvements over the reference method, respectively.

}, keywords = {psclab}, author = {Zifan Yu and Bruno Machado Trindade and Michael Green and Zhikang Zhang and Pullela Sneha and Bank-Tavakoli, Erfan and Christopher Pawlowicz and Ren, Fengbo} } @inproceedings {London, UK, title = {STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset}, year = {2022}, month = {11/2022}, address = {London, UK}, abstract = {

Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large- scale data collection, sanitization, and annotation. Moreover, the created datasets usually face the challenge of extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on\ the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 km2 of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide datasets collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.

}, keywords = {psclab}, url = {https://www.stpls3d.com/}, author = {Meida Chen and Qingyong Hu and Zifan Yu and Hugues Thomas and Andrew Feng and Yu Hou and Kyle McCullough and Fengbo Ren and Lucio Soibelman} } @article {366, title = {A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {16}, year = {2019}, month = {05/2018}, pages = {1794-1801}, abstract = {

The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e. the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91\% specificity and 90\% average accuracy over the targeted CEUS images for prostate cancer detection, which was superior (p \< 0.05) than previously reported approaches and implementations.

}, keywords = {psclab}, doi = {10.1109/TCBB.2018.2835444}, url = {https://ieeexplore.ieee.org/document/8357938}, author = {Yujie Feng and Fan Yang and Xichuan Zhou and Yanli Guo and Fang Tang and Fengbo Ren and Jishun Guo and Shuiwang Ji} } @inproceedings {28, title = {A Body-voltage-sensing-based Short Pulse Reading Circuit for Spin-torque Transfer RAMs (STT-RAMs)}, year = {2012}, month = {03/2012}, pages = {275-282}, publisher = {IEEE}, abstract = {

With scaling of CMOS and Magnetic Tunnel Junction (MTJ) devices, conventional low-current reading techniques for STT-RAMs face challenges in achieving reliability and performance improvements that are expected from scaled devices. The challenges arise from the increasing variability of the CMOS sensing current and the reduction in MTJ switching current. This paper proposes a short-pulse reading circuit, based on a body-voltage sensing scheme to mitigate the scaling issues. Compared to existing sensing techniques, our technique shows substantially higher read margin (RM) despite a much shorter sensing time. A narrow current pulse applied to an MTJ significantly reduces the probability of read disturbance. The RM analysis is validated by Monte-Carlo simulations in a 65-nm CMOS technology with both CMOS and MTJ variations considered. Simulation results show that our technique is able to provide over 300 mV RM at a GHz frequency across process-voltage-temperature (PVT) variations, while the reference designs require 4.3 ns and 2.3 ns sensing time for a 200 mV RM, respectively. The effective read energy per bit required by the proposed sensing circuit is around 195 ft in the nominal case.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6187506}, author = {Ren, Fengbo and Park, Henry and Dorrance, Richard and Toriyama, Yuta and Yang, C-KK and Markovi{\'c}, Dejan} } @article {18, title = {Scalability and Design-space Analysis of A 1T-1MTJ Memory Cell For STT-RAMs}, journal = {IEEE Transactions on Electron Devices}, volume = {59}, year = {2012}, month = {Jan.}, pages = {878{\textendash}887}, abstract = {

We present a design-space feasibility region, as a function of magnetic tunnel junction (MTJ) characteristics and target memory specifications, to explore the design margin of a one-transistor-one-magnetic-tunnel-junction (1T-1MTJ) memory cell for spin-transfer torque random access memories (STT-RAMs). Data from measured devices are used to model the statistical variation of an MTJ{\textquoteright}s critical switching current and resistance. The sensitivity of the design space to different design parameters is also analyzed for the scaling of both the MTJ and the underlying transistor technology. A design flow, using a sensitivity-based analysis and an MTJ switching model based on the Landau-Lifshitz-Gilbert equation, is proposed to optimize design margins for gigabit-scale memories. Design points for improved yield, density, and memory performance are extracted from MTJ-compatible complementary metal-oxide-semiconductor (CMOS) technologies for 90-, 65-, 45-, and 32-nm processes. Predictive technology models are used to explore the future scalability of STT-RAMs in upcoming 22- and 16-nm technology nodes. Our analysis shows that, to achieve Flash-like densities ( \<; 6F2) in advanced CMOS technologies, aggressive scaling of the critical switching current density will be required.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6140952}, author = {Dorrance, Richard and Ren, Fengbo and Toriyama, Yuta and Hafez, Amr Amin and Yang, C-KK and Markovi{\'c}, Dejan} } @inproceedings {24, title = {Scalability And Design-space Analysis of A 1T-1MTJ Memory Cell}, year = {2011}, month = {01/2011}, pages = {32-36}, publisher = {IEEE}, abstract = {

This paper introduces a design-space feasibility region as a function of MTJ characteristics and memory target specifications. The sensitivity of the design space is analyzed for scaling of both MTJ and underlying transistor technology. Design points for improved yield, density, and memory performance can be extracted for 90nm down to 32nm processes based on measured MTJ devices. To achieve flash-like densities in upcoming 22nm and 16nm technology nodes, scaling of the critical switching current density is required.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5941480}, author = {Dorrance, Richard and Ren, Fengbo and Toriyama, Yuta and Amin, Amr and Yang, C-KK and Markovi{\'c}, Dejan} }