@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 {Virtual Event, title = {MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time}, year = {2020}, month = {07/2020}, address = {Virtual Event}, abstract = {

Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocular image and draw a 3D bounding box for each object. The MoNet3D method incorporates prior knowledge of the spatial geometric correlation of neighboring objects into the deep neural network training process to improve the accuracy of 3D object localization. Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25\% and 94.74\%, respectively. Moreover, the method can realize the real-time image processing at 27.85 FPS, showing promising potential for embedded advanced driving assistance system applications. Our code is publicly available at

}, keywords = {psclab}, author = {Xichuan Zhou and Yicong Peng and Chunqiao Long and Fengbo Ren and Cong Shi} } @article {palangi2016distributed, title = {Distributed Compressive Sensing: A Deep Learning Approach.}, journal = {IEEE Trans. Signal Processing}, volume = {64}, number = {17}, year = {2016}, pages = {4504{\textendash}4518}, keywords = {Compressive Sensing, ref}, author = {Palangi, Hamid and Ward, Rabab K and Deng, Li} } @inproceedings {mousavi2015deep, title = {A deep learning approach to structured signal recovery}, year = {2015}, pages = {1336{\textendash}1343}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Mousavi, Ali and Patel, Ankit B and Baraniuk, Richard G} } @article {putnam2015reconfigurable, title = {A reconfigurable fabric for accelerating large-scale datacenter services}, journal = {IEEE Micro}, volume = {35}, number = {3}, year = {2015}, pages = {10{\textendash}22}, publisher = {IEEE}, keywords = {Deep Learning, ref}, author = {Putnam, Andrew and Caulfield, Adrian M and Chung, Eric S and Chiou, Derek and Constantinides, Kypros and Demme, John and Esmaeilzadeh, Hadi and Fowers, Jeremy and Gopal, Gopi Prashanth and Gray, Jan and others} } @article {19, title = {Healable Capacitive Touch Screen Sensors Based on Transparent Composite Electrodes Comprising Silver Nanowires and a Furan/Maleimide Diels-Alder Cycloaddition Polymer}, journal = {ACS Nano}, volume = {8}, year = {2014}, month = {Dec.}, pages = {12874{\textendash}12882}, abstract = {

A healable transparent capacitive touch screen sensor has been fabricated based on a healable silver nanowire{\textendash}polymer composite electrode. The composite electrode features a layer of silver nanowire percolation network embedded into the surface layer of a polymer substrate comprising an ultrathin soldering polymer layer to confine the nanowires to the surface of a healable Diels{\textendash}Alder cycloaddition copolymer and to attain low contact resistance between the nanowires. The composite electrode has a figure-of-merit sheet resistance of 18 Ω/sq with 80\% transmittance at 550 nm. A surface crack cut on the conductive surface with 18 Ω is healed by heating at 100 {\textdegree}C, and the sheet resistance recovers to 21 Ω in 6 min. A healable touch screen sensor with an array of 8 {\texttimes} 8 capacitive sensing points is prepared by stacking two composite films patterned with 8 rows and 8 columns of coupling electrodes at 90{\textdegree} angle. After deliberate damage, the coupling electrodes recover touch sensing function upon heating at 80 {\textdegree}C for 30 s. A capacitive touch screen based on Arduino is demonstrated capable of performing quick recovery from malfunction caused by a razor blade cutting. After four cycles of cutting and healing, the sensor array remains functional.

}, keywords = {psclab}, url = {http://pubs.acs.org/doi/abs/10.1021/nn506610p}, author = {Li, Junpeng and Liang, Jiajie and Li, Lu and Ren, Fengbo and Hu, Wei and Li, Juan and Qi, Shuhua and Pei, Qibing} } @article {20, title = {Reference Calibration of Body-voltage Sensing Circuit for High-speed STT-RAMs}, journal = {IEEE Transactions on Circuits and Systems I: Regular Papers}, volume = {60}, year = {2013}, month = {Apr.}, pages = {2932{\textendash}2939}, abstract = {

With the continuing scaling of MTJ, the high-speed reading of STT-RAM becomes increasingly difficult. Recently, a body-voltage sensing circuit (BVSC) has been proposed for boosting the sensing speed. This paper analyzes the effectiveness of using the reference calibration technique to compensate for the device mismatches and improve the read margin of BVSC. HSPICE simulation results show that a 2-bit reference calibration can improve the worst-case read margin in a 1-Mb memory by over 3 times. This leads to up to 30\% higher yield across all process corners. In order to maintain the yield improvement even in the worst-case corner, independent calibration circuitry has to be deployed for each memory array.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6492140}, author = {Ren, Fengbo and Park, Henry and Yang, C-KK and Markovi{\'c}, Dejan} } @inproceedings {palangi2013using, title = {Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors.}, year = {2013}, pages = {3337{\textendash}3341}, keywords = {Compressive Sensing, ref}, author = {Palangi, Hamid and Ward, Rabab K and Deng, Li} } @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} } @inproceedings {25, title = {Analysis of STT-RAM Cell Design With Multiple MTJs Per Access}, year = {2011}, month = {01/2011}, pages = {53-58}, publisher = {IEEE Computer Society}, abstract = {

Density of STT-RAMs is limited by the area cost and width of the access device in a cell since it needs to support the programming currents. This paper explores a cell structure that shares each cell{\textquoteright}s access transistor with multiple MTJ memory elements. Feasibility and limitations of such a cell structure is explored for both reading and writing of the memory. The analytical and simulation results indicate that only small amount of sharing is possible and having MTJs that can handle a high read current without disturbing the cell is needed.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2052103}, author = {Park, Henry and Dorrance, Richard and Amin, Amr and Ren, Fengbo and Markovi{\'c}, Dejan and Ken Yang, CK} } @inproceedings {perez2009supervised, title = {A supervised learning method for neural networks based on sensitivity analysis with automatic regularization}, year = {2009}, pages = {157{\textendash}164}, publisher = {Springer}, keywords = {Deep Learning, ref}, author = {P{\'e}rez-S{\'a}nchez, Beatriz and Fontenla-Romero, Oscar and Guijarro-Berdi{\~n}as, Bertha} }