Category Archives: Sensors

Sensor Technologies for Air Quality Monitoring

Although air is all around us, we breathe it in every minute, and our lives depend on it, yet we pay very little attention to the quality of air, unless when facing a problem. Whether it is indoors or outdoors, poor air quality can affect our health and well-being significantly. Two levels of air pollution measurement are significant here.

One is the presence of small PM2.5 or Particulate Matters measuring less than 2.5 microns in size—one micron being one-micrometer equal to one-millionth of a meter or one-thousandth of a millimeter. The other is the presence of VOCs or Volatile Organic Compounds.

Combustion processes emit PM2.5 type of pollutants, for instance, by fires burning in fireplaces and lit candles within the house. Cleaning textiles, furniture, and supplies can emit VOCs. Engineers and scientists are working on improving sensing technologies to enable monitoring PM2.5 and sensing VOC by personal air quality monitoring systems for improving the health and well-being of the people.

According to the WHO, PM2.5 enters our lungs easily causing serious health problems such as chronic and acute respiratory diseases, asthma, lung cancer, heart diseases, and stroke. A recent study by Harvard University links PM2.5 exposure to sensitivity to viral diseases such as SARS-CoV-2.

While one does receive averaged or consolidated data from official air quality monitoring stations, that data is for the outdoor environment only. For indoor air pollution monitoring, a portable air quality measuring device, also known as a dosimeter, is more appropriate—especially when incorporated within a wearable or a smartphone. So far, PM2.5 sensors were too large for mobile devices. Bosch Sensortec now has sensors that make it possible to incorporate them into personal devices.

The Bosch PM2.5 technology offers sensors small enough to incorporate within wearables and smartphones for measuring the daily exposure of a person to PM. The person can see data and trends of local pollution levels to which they are exposing themselves, and take appropriate actions to minimize their exposure for improving their health and well-being.

BreezoMeter uses PM2.5 sensor technology from Bosch Sensortec to make PM2.5 Dosimeters. They also offer an app for the Dosimeter that collates local data measured by the Bosch PM2.5 sensor and the air pollution data from the BreezoMeter to calculate and display the personal daily PM exposure.

Conventionally, PM sensors rely on a fan to draw air through a cell, where optical arrangements count the particulate matter and calculate the concentration per unit of volume. This arrangement requires the sensor to be the size of a matchbox, incapable of incorporating within a smartphone.

PM2.5 sensor technology that Bosch Sensortec has developed functions on natural ambient airflow. The principle is rather like a camera, with three lasers integrated behind a glass cover. To prevent damage to the user, Bosch uses Class 1 lasers that are eye-safe. The entire arrangement is flat enough like a smartphone camera is, making it easier to incorporate within one, and using only 0.2% of the volume of air that other solutions on the market typically use.

Pyroelectric Sensors

Certain crystalline substances are electrically polarized, and a change in heat causes them to change their polarization proportionally. The crystal manifests its change in polarization by temporarily generating a detectable voltage across itself. Scientists call the behavior of such crystals the Pyroelectric effect and the phenomenon as Pyroelectricity. Sensors made of such crystals are pyroelectric sensors and they are infrared sensors with a host of applications with the underlying technology relying on the pyroelectric effect.

With pyroelectric sensors, it is possible to detect infrared radiation or heat emanating from substances. Different materials and chemicals absorb infrared radiation at specific wavelengths. Therefore, pyroelectric sensors can detect the presence of a specific material or chemical by sensing the change in a specific wavelength of IR that the substance is blocking. Two basic types of pyroelectric sensors are available—passive and active.

Passive pyroelectric sensors can measure or detect infrared rays that an object generates as an IR emitter. Active pyroelectric sensors require the presence of an absorber between itself and the IR source, to be able to detect the wavelengths that the absorber is absorbing. The industry uses pyroelectric sensors primarily to detect motion, gas, food, and flame, among others.

Motion sensing can use either active or passive pyroelectric sensors. Active pyroelectric sensors are useful in instances where the emitter and sensor are far apart over a very long distance. A garage door safety sensor is a simple example. Anything blocking the infrared signal across the opening of the door sends a signal to stop it from lowering. Passive pyroelectric sensors can be very sensitive in detecting the source of heat directly, such as from a human body. The user can configure the sensor to detect the presence or absence of any object, including a human body, radiating enough IR.

Monitoring and detecting the presence of gasses is another popular application for pyroelectric sensors. The setup requires the presence of an IR emitter and an active sensor across a sample of the gas. The pyroelectric sensor checks for the presence of a specific wavelength—the absence of which means the gas absorbing the specific wavelength is present in the sample. Using optical IR filters, manufacturers can tune the sensors to a specific wavelength, permitting only that wavelength to pass through to the sensing element.

Like pyroelectric gas sensors, manufacturers can calibrate pyroelectric food sensors to detect food-related substances. For instance, pyroelectric food sensors can differentiate between fat, lactose, and sugar, as they absorb different IR wavelengths. In fact, these general pyroelectric sensors are useful for monitoring many types of commercial, industrial, and medical substances or processes, depending mainly on their configuration.

Pyroelectric flame sensors can easily detect flames as they are strong emitters of IR. They are useful not only in detecting the presence of flames, pyroelectric sensors can also differentiate between sources of flames. Triple IR flame detection systems do this by comparing three specific IR wavelengths, and their ratios to each other. This helps to detect flames to a high degree of accuracy—very useful in fire protection systems and in smart homes, furnace monitoring, and forest fire detection.

Coreless Magnetic Current Sensors

Modern industrial drives require accurate current measurement for effectively regulating the torque and ensuring maximization of operational efficiency levels. For achieving necessary efficiency levels along with the safety requirements, the measurement methodology must achieve a high degree of linearity and respond rapidly. This is especially true for detecting conditions such as short-circuit and over-current. For instance, it is necessary to arrest the fault condition from an over-current situation within 3us or less. The detection, evaluation, and triggering process must occur within 1 us or less. Therefore, it makes tremendous sense to include this capability within the current sensor.

A popular current measuring scheme involves using a shunt resistor in series with the current under measurement. However, this involves insertion loss, with the resistance of the PCB track, solder joints, and wiring contributing to the loss in addition to that from the shunt resistance. The design becomes more complex if the shunt resistor requires galvanic isolation between control electronics and power output stages.

A better alternative is the magnetic current sensor, primarily based on Hall effect and using core-based or core-less sensing. Being non-resistive, magnetic current sensors involve an insertion loss of a far lower amount. Moreover, magnetic current sensors are contact-less, thereby providing inherent isolation between low voltage and high voltage circuits.

A current flowing through a conductor generates a magnetic flux. A core-based sensor typically concentrates the flux in its ferromagnetic core. The open-loop configuration of the sensor typically uses a sensing element within the air-gap, where the flux concentration is the maximum. This arrangement can have hysteresis and temperature drift errors.

The closed-loop configuration has a compensation winding with current flowing in the opposite direction to minimize the hysteresis and temperature drift errors. Although providing very precise current measurements, the approach is complex and the introduction of the compensation winding generates additional power losses.

In contrast, a core-less sensor does not use a ferromagnetic core, thereby avoiding the hysteresis and temperature drift errors altogether. Current measurement now depends totally on the magnetic field that the current-carrying conductor generates. Although the flux density that the wire generates is much lower, modern electronics design easily compensates for this.

Like the core-based sensor, the core-less sensor also has an open-loop and a closed-loop design. In closed-loop sensing, compensatory windings equalize the flux density and use Hall element sensing. The open-loop sensing uses highly linear Hall elements. Therefore, closed loop sensing does not depend on the linearity of its Hall elements.

With core-less sensors using very low levels of flux density, industrial environments with EMI often makes it difficult to measure the current accurately. Shielding improves the situation to a certain extent, but may not be totally adequate.

A differential measurement approach resolves the situation. This requires a suitable conductor structure along with the presence of at least two sensor elements arranged with their sensitivities in perpendicular. If the electrical connection has the polarities of the sensors opposing each other, and the positioning of the elements above the conductor is symmetrical, they effectively cancel the common-mode component of any external stray fields that may disturb the current measurement.

Sensitive Magnetic-Field Sensor Has Low Noise

Although applications for magnetic sensors cover a vast field, ranging from the gigantic magnetic resonance imaging or MRI systems to sensing tiny gear-teeth, they are one of the most overlooked or misunderstood among the modern sensors in use. Researchers are constantly on the lookout for increasingly small but more sensitive magnetic-field sensors. However, sensitivity alone is not the only qualifying parameter for such sensors—low-level transducers require to be low-noise as well.

That is exactly what researchers at Brown University have developed. Their magnetic sensor is not only sensitive, it exhibits a very low noise level. With support from the National Science Foundation, the researchers have developed a device that, as a part of an arrangement of a magnetic immunoassay, looks for pathogens in fluid systems using magnetism. They claim that as the device is extremely small, millions of such sensors can fit on a single chip.

The basic principle behind the sensor is the Hall effect. In a Hall effect sensor, passing a direct current through it when the sensor is perpendicular to a magnetic field, causes the development of a voltage at right angles to the current path. The presence and magnitude of the magnetic field directly influence the presence and magnitude of the voltage.

The researchers at Brown University have developed a variation of the Hall effect sensor and have named it the Anomalous Hall Effect or AHE, and this occurs in ferromagnetic materials only. The difference between the two effects is that while the conventional Hall effect is the result of charge on electrons, the anomalous Hall effect is due to electron spin.

As electrons with various spins orient themselves in different directions, the AHE detects this with a small but definite voltage. Incidentally, magnetic fields cause many interesting phenomena on atomic particles. For instance, MRI systems capture signal source emissions related to the magnetic moment of the hydrogen nucleus.

The researchers fabricated the device as an ultra-thin film made of ferromagnetic materials like boron, iron, and cobalt, with electron spins arranged in in-plane anisotropy—meaning, the electron spins align themselves in the plane of the film. However, exposing the film to a high temperature and a strong magnetic field can change the spin of the electrons to perpendicular anisotropy, and their alignment turns perpendicular to the film.

Equalizing the two anisotropies results in a reorientation of the electron spins when the material encounters any external magnetic field, providing a reorientation voltage across the AHE. Compared to a conventional Hall-effect sensor, an AHE sensor is about 20X more sensitive.

The thickness of the AHE device offers a tradeoff in performance. A thick film requires a strong magnetic field to reorient the spins, resulting in a reduction in sensitivity. However, in a thin film, the electrons tend to reorient their spins by themselves, reducing the usefulness of the sensor. The researchers tried many thicknesses and found 0.9 nm thickness worked the best.

As magnetic anisotropy is highly dependent on temperature, researchers are using temperature to fine-tune a single magnetic AHE sensor, thereby achieving very low levels of intrinsic noise during its operation.

Near-Zero-Power Sensors

The US Department of Defense has a research and development agency, the defense advanced research projects agency or DARPA, which have recently concluded a program N-ZERO—the near-zero power rf and sensor operations. One dramatic outcome of this program is sensors in the battlefield earlier running out of power in weeks or months can now keep operating for more than four years before they need a replacement of their coin batteries.

DARPA’s initiatives began about five years back, with the aim of improving IoT battery power so that sensors could operate in the field without requiring frequent battery replacement. The army deploys sensors in the field for detecting battlefield signals like sound, light, and vibrations.

According to DARPA, it is possible to improve battery lifetimes by using sensors that are idle most of the time, waking up to a triggering event, or periodically monitoring battlefield events as they happen, rather than monitoring them continuously.

In the field, troops can now gather data and intelligence from potential combat zones without venturing there personally. They also do not have to move into hazardous areas frequently for replacing dead batteries for sensors.

According to Benjamin Griffin, program manager of DARPA’s Microsystems Technology Office, unattended, untethered systems can now benefit from the N-ZERO program using idle but continuously alert sensing capabilities. Radiofrequency signatures or specific physical signals can trigger these sensors. As the sensor lifetime now extends to years, deployment of such sensing technologies can now be more cost-effective and safer in areas that lack fixed-energy infrastructure.

With progressive improvements in sensor technology, sensor capabilities are continuously expanding. As explained by Griffin, the N-ZERO program has been successful in developing sensors that wake up with infrared, acoustic, and RF signals. These near-zero-power sensors detect thermal radiation, measure sound levels, and communicate with radio frequency signals.

Even when coin-cell batteries power these sensors, their power consumption was so low the estimated battery life could extend from a few weeks to four years. However, during testing, processing, and communications of confirmed events limited the N-ZERO initiatives. Ultimately, the self-discharging of the batteries would also be another limiting factor.

The DARPA initiative has also been effective in developing an ARM M0N0 processor with ultra-low-power capabilities. The processor consumes only 10 nW when idling, and 20-60 µW/MHz when active, depending on the application. As sensors shutdown, they store the information in read-only memories (ROM), which the users can access without any power penalty, as the data resides in non-volatile memory.

As their power consumption is so low, the ARM processor can run for decades on a single set of batteries. Conventional coin batteries can have a lifetime of several years if the sensors operate in sleep mode, but not much beyond.

Sensors that operate continuously consume a lot of power, but often spend time processing useless data. In contrast, low power consumption processing options such as the ARM processor can cut costs when replacing failed batteries frequently. An example implementation for processing audio files could run continuously for more than 200 days when powered by a single LR44 coin-cell battery.

Contactless Magnetic Angle Sensing

Contactless magnetic angle position sensors are now giving optical encoders a run for their money. This was recently demonstrated by Monolithic Power Systems at Electronica 2018. They had on display a unique non-automotive-focused electric vehicle, mCar, with motion control and angular sensors. According to MPS, their mCar demos two main functions—motor control elements, and angular position sensors.

As Quitugua-Flores, the mechanical engineer and primary designer of the mCar at MPS explains, the steering of the car is a complete drive-by-wire concept, and there is no mechanical connection between the tires and the steering wheel. A magnetic angle sensor detects the angle of the steering wheel and converts the signal to control the tire angle necessary for the various steering modes. The magnetic angle sensor provides visible feedback via a blue LED mounted on the dashboard, with the LED lighting up when the driver turns the steering.

An electronic system takes in the magnetic angle sensor information and feeds it wirelessly to the rest of the car, thereby instructing the wheels to turn. The angular sensor, along with the board and antenna for sending the wireless signals is attached to the steering column.

The throttle and brake pedals use similar rotary magnetic angle sensors and send their signals wirelessly just as the sensor on the steering wheel does. Pivots on the brake and acceleration pedals house the angular sensors, and they measure the angle of depression of the pedals.

However, MPS has made the mCar as an R&D application, and they have not yet approached the National Highway Traffic Safety Administration (NHTSA) for compliance with their safety regulations.

According to Quitugua-Flores, another aspect of the mCar is its driver seat pivots freely. The front and rear suspension modules keep the seat suspended such that when the mCar enters a curve, the seat tilts into the turn just as it happens in a motorcycle or a plane. This keeps the driver firmly in the seat in a turn, rather than being literally pushed out of it.

An angle sensor attached to the seat detects the rotational position and sends the information to the suspension control. The shock absorbers in the mCar come with individual integrated BLDC motors that can change the length of the shock absorbers independently. Therefore, the suspension has complete control over camber or the vertical tilting of each wheel. As the frame of the mCar tilts when turning, the suspension changes such that each tire tilts in a corresponding direction—just as a four-wheeled motorcycle does.

Shafts suspending the driver cockpit also have angular sensors attached to them. This allows the driver to enjoy a smooth ride by controlling the behavior of the suspension.

According to MPS, the mCar is only a demonstration for the effective operation of a sensing and motion control for a demo Electric Vehicle but is not a high-precision application. For systems requiring high-precision applications, MPS has demonstrated a robotic arm that allows seven degrees of freedom.

With sixteen angular sensors inside it, the arm demonstrates the capabilities of the current generation of MPS angular sensors for precision applications.

Metamaterials Improve LIDAR

Light Detection and Ranging or LIDAR is a remote sensing method. The technique uses the time of flight of pulsed laser light to measure variable distances. Airborne systems record additional data, which, when combined with the data from the light pulses are able to generate three-dimensional information about the neighboring environment that offer precise surface characteristics.

In general, a LIDAR comprises a laser, a scanner, and a specialized receiver for Global Positioning System or GPS. Although so far, common platforms for LIDAR used helicopters and airplanes for acquiring data over broad areas, autonomous vehicles are now using Topographic LIDAR extensively for navigation through road traffic using a near-infrared laser to map the nearby area.

Using LIDAR systems help scientists and engineering professionals examine both artificial and natural environments with precision, accuracy, and flexibility. As the market for LIDAR is still in its nascent state and its technologies fragmented, there are only about 70 LIDAR companies worldwide, making it a hotbed of new technology.

For scanning a wide area, conventional LIDAR systems have to rely on electro-mechanical spinners to steer laser light beams. Not only does this method reduce the scan speed, but it also affects measurement accuracy. A Seattle-based, venture-backed startup, Lumotive, is now developing a new technology that will change the way LIDAR systems function.

According to Bill Colleran, co-founder, and CEO of Lumotive, they are developing a LIDAR system that can steer beams but has no moving parts. Rather, their patented technology uses the light-bending properties of metamaterials such as Liquid Crystal Metasurfaces or LCM to steer the laser beams. Bill calls the use of such metamaterials “pivotal technology.”

However, Lumotive is not the only player in the field to offer LIDAR systems that do not rely on mechanical scanning. Other rivals have used optical phased arrays or MEMS mirrors to claim their LIDARs use a lower number or no mechanical components.

According to Bill, Lumotive LIDAR systems use LCM semiconductor chips. The main advantages of LCM are it offers a large optical aperture of about 25 x 25 mm, resulting in a longer range for the LIDAR, along with a 120-degree field of view. The high performance of the LCM comes from its fast-random-access beam steering capability.

When a laser beam shines onto the Lumotive’s liquid crystal metasurface chip, programmed electrical signals can direct the reflected light into any direction within its 120-degree field of view.

Metamaterials are mostly artificially structured materials that allow unprecedented control over their properties, specifically in new ways for controlling the flow of electromagnetic radiation including light. For instance, Kymeta has a flat-panel satellite antenna technology based on metamaterials.

Kymeta’s antenna can move electronically. It does not require the conventional phase shifters, amplifiers, and related components on its surface. This not only cuts down the cost, it also consumes far less power and does not require cooling devices. Compared to conventional antenna systems, Kymeta is able to increase the density of their flat-panel antenna elements dramatically, while controlling the phase and amplitude simply by activating or deactivating individual antenna elements. Lumotive have adapted the Kymeta antenna’s metamaterial architecture to their LIDAR system.

Thermopile Sensors for IoT

mart and connected technologies are presently driving the astonishing growth of the Internet of Things (IoT). However, growth in these technologies is, in turn, a result of the tremendous development of various sensors. According to the Boston Consulting Group, by 2020, expect to spend US$ 265 billion for IoT technologies, services, and products. Much of this growth will owe its progress to that of sensors.

One can now find sensors almost everywhere, for instance, in smart retail, smart healthcare, and smart homes. Today, most people start their day with pressing a couple of apps while still in bed, thereby turning on the high-end coffee maker for the first-morning cup, or their night adjusting the climate control zoning system for keeping the bedroom in that ideal sleeping temperature.

As an example, a large health insurer in Australia is placing sensors throughout the house of elderly members for monitoring their health and preventing them from falling. They place sensors within refrigerators, medicine cabinets, bathrooms, and doorways. The sensors monitor movement by tracking the temperature within the home. Any break in routing such as a change in the temperature notifies the family immediately.

Viewers of professional golfing can see information on the heart rates of the players on their TV screens thanks to a special camera and sensors monitoring the faces of the players. This contactless vital sensing technique allows TV viewers to read the stress levels of the athletes as they play.

The past decade has seen a drastic drop in the prices of sensors as a result of the advancement of technology. This reduction has exponentially increased the use of sensors not only in civilian applications, but also in military, aerospace, and in collision avoidance systems in the automotive industry.

Advances in complex micro-electro-mechanical systems (MEMS) and thermopiles are improving uncooled IR sensor technology. This MEMS-based technology offers free-standing thermal isolation structures surrounding a printed thin-film IR absorber. This allows the collection of radiated power to determine the temperature of a remote object. Using semiconductor technology, it is now possible to add hundreds on thermocouples on several square millimeters of a thermopile sensor. Besides being small and reasonably priced, these thermopile array sensors are smart enough to be accurate with faster response time. It makes them ideal for building automation, people counting, security systems, medical instruments, and more.

For instance, the 8×8 thermopile array device is a sensor with 64-pixel IR sensors fitting within a surface mount package that can withstand reflow soldering. Apart from a silicon lens that collects the infrared energy, the package consists of a digital ASIC, a MEMS detector chip, and RF-shielded metal cover, and an I2C interface.

While operating, the thermopile array sensor has a 60-degree field of view for absorbing emitted thermal energy. The 64 sensing elements in the array individually convert the absorbed thermal energy to produce a proportional output signal. After amplification, an ADC converts these analog temperature signals to digital, while also referencing them against the ambient temperature value measured by a thermistor. A microprocessor collects the digital data and proceeds to map the temperature from individual thermopile elements into a thermal representation of the entire field of view.

Why you need Sensor as well as PLC Data

In the Industry, collection of IoT data, specifically that from manufacturing processes is very important. Apart from the quantity of the data collected, the quality of information from various machines is also equally vital for analysis, and to make decisions.

IIoT puts a lot of stress on the usefulness of predictive analytics based on big data. According to the Forbes magazine, big data offers the volume, speed, and variety of information about important effects that traditional methods of empirical research and the human eye is unable to capture. Therefore, big data becomes the primary step towards generating valuable insights from evidence-based interventions. From a theoretical and practical perspective, big data not only helps to predict outcomes, but it also helps in explaining them, especially in understanding the underlying causes.

Companies usually build plug-n-play adapters for controls, thereby enabling them to capture hundreds of data points directly from PLCs. Although this generates vast quantities of data for analysis, and a large part of it will be helpful as deep data, there will always be some part of the data that will remain useless, as will some results.

By taking the analysis down to a more granular level, deep data can eliminate irrelevant information and focus on the streams for a certain course of investigation. Analyzing deep data offers more accurate overall predictive trends.

Data from a specific sensor on a machine offers a snapshot within a designated timeframe. Sensor data monitors specific situations, such as vibrations that signify to an operator the state of operation of the machine—on versus off. However, all sensor data may or may not be useful during a review or analysis.

On the other hand, PLCs can collect large amounts of data, and when combined with sensor data, allows the operator to gather a full picture of the machine status at any time. This data can help to monitor inputs to and outputs from a machine, and based on programming, can make logical decisions when necessary.

Older machines with legacy controls and those with no controls need additional integration/hardware support for capturing data. While auxiliary hardware can capture digital and analog IO, adding sensors can generate additional data points.

The ability to capture deep PLC data and data from sensors that monitor specific items that the PLC cannot reach forms the basis of high-quality analytics and results—all the more reasons for the necessity of sensor as well as PLC data.

For instance, while a sensor may provide information on the vibration limits of a certain machine or parts thereof, the PLC data from the machine may include parameters signaling an impending fault. Therefore, the PLC data offers the ability to control the operation of or sequence of activity of a nearby a machine. When the sensor data signals one or more parameters are beyond the programmed limits, the operator can respond quickly, and need not wait for analysis.

Therefore, using both sets of data from sensors as well as from PLCs offers more information to the user than either on their own do. This allows the operator greater flexibility for avoiding expensive downtimes and maintenance issues.

What are Linear Image Sensors?

Fairchild Imaging makes CMOS 1421, a linear image sensor. This is an imaging device with a wide dynamic range of 94 dB or 52000:1, with excellent linearity. The device is a linear sensor, meaning it has 2048 x 1 high-resolution imaging sensors. Fairchild has designed this linear sensor for medical and scientific line scan applications such as optical inspection or fluorescent imaging that require wide dynamic range, high sensitivity, and low noise operation.

With several acquisition modes, this photodiode pixel has an optical area measuring 7 x 10 µm with a pitch of 7 µm and a fill factor of 85%, making the operation of this sensor very flexible:

  • Read after Integration: This mode is ideal for applications with high quality signals
  • Buffered Read after Integration: is a high speed mode that integrates the next line while reading the current line
  • Read on Integration: This is a non-CDS mode, allowing the highest speed of operation
  • Multiple Read during Integration: This mode is for low-light applications, permitting oversampling during integration

Other than the above, a programmed mode, accessible through JTAG interface, meets a wide range of specialized imaging requirements. Readout cycles in this mode are controllable through external signals.

CMOS 1421 has several features such as very low dark current, very low readout noise, and non-destructive readout for fowler sampling. Along with anti-blooming drain and electronic shutter, the CMOS 1421 also features two independent gain settings for each pixel. The entire device is enclosed in an RoHS compliant CLCC and PLCC package of 22.35 x 6.35 x 2.85 mm dimensions. The device consumes 40 mW of power while operating from 3.3 VDC. Major applications of linear image sensors are in microscopy, photon counting, and fluorescent imaging.

CMOS 1421 has a pixel array consisting of a photodiode, a pixel amplifier, and a sample and hold circuit. Along with the above, each pixel has a noise suppression circuitry and a gain register. While the pixel-level gain affects the device sensitivity, it also has a bearing on the noise and conversion factor of the sensor.

Linear image sensors from Fairchild use thinned back-illuminated large area arrays. Fairchild offers custom capabilities such as extreme spectral band detection, low noise active reset CMOS architecture, and high-resolution X-ray imagery using these sensors.

These linear image sensors are ideal for visible, ultra violet to visible, and visible to near infrared spectrometers, and their enhancement makes them suitable for spectroscopy applications. The design of their pixels being tall and narrow helps light distribution from a spectrometer’s grating. If provided with UV sensitivity, these sensors do not need extra UV coating.

CMOS 1421 displays superior linearity, which is of extreme benefit to spectroscopy measurements. The device also includes an electronic shutter along with a built-in timing generator, which are useful in spectroscopy. The device is suitable for several applications involving scientific, industrial, and commercial activities.

New sensors based on CMOS match features with those of CCDs. Featuring simpler external circuit design, and simpler operation, CMOS 1421 linear image sensors are suitable for spectroscopy, displacement measurement, barcode scanning, and imaging.