CMOS Smart Imagers and Vision Chips.
Complementary Metal-Oxide-Semiconductor (CMOS) smart imagers and vision chips play a crucial role in various applications, including digital cameras, medical imaging, surveillance systems, and automotive safety systems. These devices are designed to capture and process visual information efficiently. Here are some key aspects and features associated with CMOS smart imagers and vision chips:
CMOS Technology:
CMOS is a semiconductor technology that integrates both digital and analog functions on a single chip.
CMOS sensors are commonly used in imaging applications due to their low power consumption, high integration capability, and relatively low manufacturing costs.
Smart Imagers:
Smart imagers typically refer to image sensors that incorporate additional on-chip processing capabilities beyond simple image capture.
These sensors can include features such as signal processing, image compression, and even basic image analysis functions directly on the sensor chip.
Vision Chips:
Vision chips go beyond simple image sensors and often refer to more complex devices that include not only image sensors but also dedicated processing units for advanced image processing and computer vision tasks.
These chips are designed to enable real-time analysis and interpretation of visual data.
Applications:
Consumer Electronics: CMOS imagers are widely used in digital cameras, smartphones, and other consumer electronics devices for capturing images and videos.
Medical Imaging: In medical applications, CMOS imagers can be used in endoscopy, microscopy, and other diagnostic imaging devices.
Automotive: Vision chips play a crucial role in automotive safety systems, including advanced driver assistance systems (ADAS) and autonomous vehicles.
Industrial and Surveillance: CMOS vision chips find applications in industrial inspection, surveillance cameras, and robotics.
Features and Advantages:
Low Power Consumption: CMOS technology generally consumes less power compared to alternative technologies like CCD (Charge-Coupled Device).
Integration: The integration of processing capabilities on the chip reduces the need for external components, making the overall system more compact.
Cost-Effectiveness: CMOS manufacturing processes are often more cost-effective than other technologies, contributing to the widespread adoption of CMOS-based imagers.
Image handling is instrumental in many applications, including consumer electronics, surveillance, robotics, machine vision, etc. Some of them demand high quality images, while others require fast analysis and interpretation of the image flow. Despite the specific target, all applications benefit from embedding processing circuitry together with optical sensors in the same silicon substrate. CMOS technologies allow the incorporation of digital processing on-chip to correct image artifacts or to analyze and interpret the scene in real-time. Using CMOS technologies enables the implementation of cameras and vision systems with reduced power consumption and reduced size. This permits the incorporation of vision in applications where it was previously considered to be economically prohibitive or technically unfeasible.
This research line embraces different activities related to the incorporation of intelligence into image sensors, namely:
- New pixel topologies for enhanced sensitivity and reduced noise.
- Front-side and Back-side illuminated sensors.
- Pixels for single-photon detection and time-of-flight calculations.
- Pixels for high-dynamic range image acquisition.
- In-pixel processing and memory for feature extraction at the focal-plane.
- Re-configurable read-out channels for high-performance digital imagers.
- Data converters for high-speed and high accuracy (low noise) image downloading.
- Architectures and algorithms for on-chip image correction.
- Distributed, progressive processing architectures for vision systems.
- Sensors for 3-D image capture.
Different application areas are covered like automotive, unmanned vehicle navigation, distributed smart cameras and vision-enabled wireless sensor networks. These applications have been benchmarked by using real systems. Significant parts of the technology have been transferred to industry, including the creation of spin-off companies.
Contact
J. Fernandez-Berni, R. Carmona-Galan and A. Rodriguez-Vazquez, Low-Power Smart Imagers for Vision-Enabled Sensor Networks, Springer, 2012 » doi
http://www.imse-cnm.csic.es/en/introduction.php