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Image and Signal Sensors for Computing and Machine Vision: Developments to Meet Future Needs

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Abstract

Image sensors used in current machine vision systems suffer from low dynamic range and poor colour constancy and are brittle and unmalleable, limiting their use in applications for which there will be considerable demand in the future. Most approaches aiming to resolve these inadequacies focus on developing improvements in the lighting, software (processing algorithms) or hardware surrounding the photosensor such as the filters. Other strategies involve changing the architecture of the image sensor and the photosensing material; both have experienced recent success. Although they are yet to break fully into the market, image sensors developed from alternative solution-processed materials such as organic semiconductors and organohalide perovskites have immense potential to address the above issues and to ‘disrupt’ machine vision technology.

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Abbreviations

ADC:

Analogue-to-digital convertor

ASIC:

Application-specific integrated circuit

CCD:

Charge-coupled device

CFA:

Colour filter array

CIS:

CMOS image sensors

CMOS:

Complementary metal-oxide semiconductor

CQDs:

Colloidal quantum dots

D/A:

Donor–acceptor

D ∗ :

Specific detectivity

EQE:

External quantum efficiency

FET:

Field-effect transistor

FIT:

Frame interline transfer

FT:

Frame transfer

FWHM:

Full width at half maximum

ICP:

Integrated colour pixel

IoTs:

Internet of things

IR:

Infrared

J d :

Dark current

J ph :

Photocurrent

LDR:

Linear dynamic range

MVS:

Machine vision systems

NEP:

Noise-equivalent power

OFET:

Organic field-effect transistor

OHP:

Organohalide perovskite

OLED:

Organic light-emitting diode

OPD:

Organic photodiode

OPT:

Organic phototransistor

OSC:

Organic semiconductor

PT:

Phototransistor

RGB:

Red green blue (referring to a colour filter system)

ROIC:

Read-out integrated circuitry

TFD:

Transverse field detector

ToF:

Time of flight

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Authors and Affiliations

  1. Department of Chemistry, Queen’s University, Kingston, ON, Canada

    Ross D. Jansen-van Vuuren

  2. School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia

    Ali Shahnewaz & Ajay K. Pandey

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  2. Universidad Autónoma de Baja California, Mexicali, Baja California, Mexico

    Wendy Flores-Fuentes

  3. Leuphana University of Lueneburg, Lueneburg, Low Saxony, Niedersachsen, Germany

    Paolo Mercorelli

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Jansen-van Vuuren, R.D., Shahnewaz, A., Pandey, A.K. (2020). Image and Signal Sensors for Computing and Machine Vision: Developments to Meet Future Needs. In: Sergiyenko, O., Flores-Fuentes, W., Mercorelli, P. (eds) Machine Vision and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-030-22587-2_1

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