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Special Issue "Smart Image Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Prof. Christophe Bobda
Website
Guest Editor
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USA
Interests: computer architecture; embedded systems; embedded vision intelligence; cybersecurity; FPGA and high-performance computing
Prof. Marilyn Wolf
Website
Guest Editor
Department of Computer Science and Engineering, University of Nebraska-Lincoln, 1400 R Street Lincoln, NE 68588, USA
Interests: cyberphysical systems; Internet of Things; embedded computing; embedded computer vision; VLSI systems
Prof. Saibal Mukhopadhyay
Website
Guest Editor
School of Electrical and Computer Engineering, Georgia Institute of Technology, North Avenue, Atlanta, GA 30332, USA
Interests: low-power, variation tolerant, and reliable VLSI systems; device/circuit level modeling/estimation of power, yield, and reliability; technology-circuit co-design methodologies; self-adaptive systems with on-chip sensing and a repair technique; memory design for VLSI applications; ultralow power and fault-tolerant nanoelectronics: technology, circuit, and computing platforms

Special Issue Information

Dear Colleagues,

Cameras are pervasively used in a wide range of applications, including monitoring and surveillance, crowd analysis, traffic control, precision agriculture, remote sensing, and manufacturing. While increasing the resolution of image sensors allows to capture tiny details of remote landscape and events, the fast-growing amount of data generated by modern image sensors is outpacing our capability to transport, store, and extract relevant content. Comparable to the 72 gigabytes that human vision sends to the brain every second, the amount of data produced by such systems in the future poses two major challenges: transportation and computation. First, current and future communication systems, even with the most advance video compression architecture, will not be able to provide the required bandwidth to transport those huge data streams. Second, the extraction of relevant information from a large amount of noisy video data poses substantial challenges that can be overcome only by highly specialized computers. While post-priori and non-real-time video analysis may be enough for certain groups of applications, it does not suffice for applications such as driving assistance, surveillance, or on-board remote sensing using cameras on drones, which require near real-time video and image analysis. The goal of this Special Issue is to explore ongoing work aimed at tackling the big data challenge in future imaging applications by pushing computation closer to image sensors and exploit the massive parallel nature of sensor arrays to filter out noisy data early in the capture process and provide only structure data to high-level processing and knowledge inference stages.

We are interested in vertically integrated technology, such as focal plane sensor processors (FPSP) and vision sensors that incorporate massively-parallel and possibly hierarchical architecture in the sensor, along with artificial intelligent algorithm to directly infer the scene at the source of data. The following topics are of interest.

  • Advanced image sensor architectures
  • In-Sensor computation for image processing applications
  • Integrated learning and knowledge inference in image sensors
  • Technology and fabrication

Prof. Christophe Bobda
Prof. Marilyn Wolf
Prof. Saibal Mukhopadhyay
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image sensors
  • focal plane computation
  • knowledge inference
  • machine learning

Published Papers (3 papers)

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Research

Open AccessArticle
Depth-of-Field-Extended Plenoptic Camera Based on Tunable Multi-Focus Liquid-Crystal Microlens Array
Sensors 2020, 20(15), 4142; https://doi.org/10.3390/s20154142 - 25 Jul 2020
Abstract
Plenoptic cameras have received a wide range of research interest because it can record the 4D plenoptic function or radiance including the radiation power and ray direction. One of its important applications is digital refocusing, which can obtain 2D images focused at different [...] Read more.
Plenoptic cameras have received a wide range of research interest because it can record the 4D plenoptic function or radiance including the radiation power and ray direction. One of its important applications is digital refocusing, which can obtain 2D images focused at different depths. To achieve digital refocusing in a wide range, a large depth of field (DOF) is needed, but there are fundamental optical limitations to this. In this paper, we proposed a plenoptic camera with an extended DOF by integrating a main lens, a tunable multi-focus liquid-crystal microlens array (TMF-LCMLA), and a complementary metal oxide semiconductor (CMOS) sensor together. The TMF-LCMLA was fabricated by traditional photolithography and standard microelectronic techniques, and its optical characteristics including interference patterns, focal lengths, and point spread functions (PSFs) were experimentally analyzed. Experiments demonstrated that the proposed plenoptic camera has a wider range of digital refocusing compared to the plenoptic camera based on a conventional liquid-crystal microlens array (LCMLA) with only one corresponding focal length at a certain voltage, which is equivalent to the extension of DOF. In addition, it also has a 2D/3D switchable function, which is not available with conventional plenoptic cameras. Full article
(This article belongs to the Special Issue Smart Image Sensors)
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Open AccessArticle
Development of Reliable, High Performance WLCSP for BSI CMOS Image Sensor for Automotive Application
Sensors 2020, 20(15), 4077; https://doi.org/10.3390/s20154077 - 22 Jul 2020
Abstract
To meet the urgent market demand for small package size and high reliability performance for automotive CMOS image sensor (CIS) application, wafer level chip scale packaging (WLCSP) technology using through silicon vias (TSV) needs to be developed to replace current chip on board [...] Read more.
To meet the urgent market demand for small package size and high reliability performance for automotive CMOS image sensor (CIS) application, wafer level chip scale packaging (WLCSP) technology using through silicon vias (TSV) needs to be developed to replace current chip on board (COB) packages. In this paper, a WLCSP with the size of 5.82 mm × 5.22 mm and thickness of 850 μm was developed for the backside illumination (BSI) CIS chip using a 65 nm node with a size of 5.8 mm × 5.2 mm. The packaged product has 1392 × 976 pixels and a resolution of up to 60 frames per second with more than 120 dB dynamic range. The structure of the 3D package was designed and the key fabrication processes on a 12” inch wafer were investigated. More than 98% yield and excellent optical performance of the CIS package was achieved after process optimization. The final packages were qualified by AEC-Q100 Grade 2. Full article
(This article belongs to the Special Issue Smart Image Sensors)
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Open AccessArticle
Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network
Sensors 2020, 20(11), 3101; https://doi.org/10.3390/s20113101 - 30 May 2020
Abstract
This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for [...] Read more.
This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for computing convolution, max-pooling, and correlated double sampling operations without operational transconductance amplifiers. In addition, we used the voltage-mode MAX circuit for max pooling in the analog domain. After the analog convolution processing, the image data were reduced by 99.58% and were converted to digital with a 4-bit single-slope analog-to-digital converter. After the conversion, images were classified by the fully connected processor, which is traditionally performed in the digital domain. The measurement results show that we achieved an 89.33% image classification accuracy. The prototype CIS was fabricated in a 0.11 μm 1-poly 4-metal CIS process with a standard 4T-active pixel sensor. The image resolution was 160 × 120, and the total power consumption of the proposed CIS was 1.12 mW with a 3.3 V supply voltage and a maximum frame rate of 120. Full article
(This article belongs to the Special Issue Smart Image Sensors)
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