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Robots and machine vision devices use a variety of feedback mechanisms to assure accuracy. Discerning 3-D spaces with reasonably good accuracy can be done in several ways. So far, optical, sonic, and mechanical vision and sensing techniques have been employed with adequate levels of success. More demanding requirements force us to sharpen our already sharp pencils. Next-generation vision-based designs center on more precise depth and volume sensing with higher accuracy.
Techniques used so far have been okay for the problems at hand. The lowest cost mechanical depth or surface sensors can be as simple as a spring-loaded linear trimpot or limit switch.
When it comes to precision, sonic and optical techniques have proven more resolute without the need for moving parts. Optical distance sensing is used for simple proximity detection and more precision range finding. Proximity from less than a millimeter to 8 meters can be discerned as a digital go/no-go signal indicating the presence or absence of a target.
Thanks to the low-cost, high-resolution modern generation of cameras, video techniques have been at the forefront of distance and volumetric measurement. Next-generation design and requirements push device manufacturers to offer high-performance solutions.
In addition to machines needing higher precisions, the post-pandemic world created the need to detect people and occupancy numbers in a given location. Spacing between people is a relatively new requirement many must incorporate. A similar condition requiring attention is that of dementia. In assisted living facilities, the ability for an all-encompassing computer system to track wandering patients’ locations is crucial.
Industrial and factory applications too can take advantage of more robust and accurate distance and volume measurement subsystems. As more advanced fabrication technologies progress and merge, feedback on accuracy, position, direction, speed, and depth becomes critical for next-generation fabrication machines. For example, milling machines rely on precise motors and gear assemblies to correctly position cutting and grinding heads. Too deep, and a cutting head will break. Too shallow, and too much material is left. These machines will hit the right spot with accurate distance sensing, even if calibration is off. Closed-loop feedback produces better results. CNC machines, 3-D printers, and laser/plasma cutting and welding machines also benefit from higher accuracies of closed-loop feedback.
Analog Devices anticipated a growing need for volumetric sensing and measurement over many applications. The AD-FXTOF1-EBZ is a dedicated modular video engine with embedded Time of Flight (ToF) distance measurement built-in (Figure 1).
Figure 1: The modular 3-D sensing development kit supports various applications from volumetric measurement to occupancy and activity detection. (Source: Analog Devices)
The VGA resolution of 640x480 at 30 frames per second allows easy integration as a peripheral function to a host application supervisor. It features a two-lane Mobile Industry Processor Interface (MIPI) that can use a 25-pin or 15-pin flex cable to an interposer board.
The 940nm IR laser is an eye-safe vertical-cavity surface-emitting laser (VCSEL), which reduces manufacturing costs by eliminating the right-angle emitter configuration. It also touts its ability to operate in high light conditions thanks in part to the optical 940nm bandpass filter. This helps block noise and interference from external sources. A batwing style diffuser is used to provide the receiving lens with a precise 87-degree by 67-degree field of view.
Performance-wise, the video depth finder has two settable ranges it can operate within. A 20cm to 180cm range and a 50cm to 300cm range maintain a 2 percent accuracy. It will require a 5V 2A power supply rated from -20ºC to +75ºC, making it somewhat tough and rugged environmentally.
The SDK development kit style interface allows it to connect to a host microprocessor, microcontroller, or single-board computers like Raspberry Pi or Nvidia (Figure 2). The SDK also provides OpenCV, Open C/C++, Python®, MATLAB®, Open3D, and RoS wrappers so that developers can use them to simplify application development. Connection options include USB, Ethernet, or Wi-Fi, and reference design and bill of materials are available.
Figure 2: The actual camera and lens RFPC board and image processing AFE boards use IIC interfaces for control and configuration. Operational GPIO and MIPI interfaces allow real-time control and data access. (Source: Analog Devices)
The need for fast and resolute image and distance sensing allows designers to create next-generation sensors, robots, vehicles, and safety systems. The AD-FXTOF1-EBZ from Analog Devices lets you test the water quickly and easily. Expect higher resolution, faster frame rates, and longer distances with future versions of this technology as it gets adopted across different home and industrial applications.
The recent emergence of micromachined complementary metal oxide semiconductor (CMOS) transistors, referred to as Thermal MOS (TMOS), promises significant advancements in the realm of uncooled thermal infrared (IR) sensors. TMOS sensors consist of a thermally isolated suspended transistor designed to absorb thermal radiation, leading to increased transistor temperatures that modify the device’s I-V (current-voltage) characteristics.
What distinguishes TMOS from other traditional thermal IR sensors is its active sensing element, yielding superior temperature sensitivity. Given the exponential growth of the Internet of Things (IoT) and the ubiquity of smart home technologies, the demand for efficient, cost-effective thermal sensors—especially for uncooled IR sensors—has surged.
TMOS, with its compatibility with micro-electro-mechanical systems (MEMS) technology, paves the way for seamless integration with standard CMOS processes and is poised to revolutionize contactless temperature measurement applications.
This article delves into the nuances that differentiate TMOS sensors from the more traditional passive infrared (PIR) and thermopile sensors and considers whether these other sensors are still a viable option for new thermal sensing applications.
Before delving into the inner workings of thermal IR sensors, let’s review what IR radiation is.
IR is part of the electromagnetic spectrum (EM) that encompasses all types of radiation. EM radiation is present everywhere and is a form of energy best described as a flow of weightless particles known as photons. These photons move in a wave-like manner in a vacuum at the speed of light. Each photon carries a specific amount of energy, and those energy levels distinguish the various types of radiation. Radio waves (AM and FM) have low-energy photons, while microwave photons possess slightly more energy than radio waves. IR photons have even higher energy, followed by visible light, ultraviolet rays, X-rays, and finally, the most energetic of all, gamma rays (Figure 1).
Figure 1: The electromagnetic spectrum (EM) encompasses all types of radiation according to their energy level. (Source: Inductiveload, NASA: https://commons.wikimedia.org/wiki/File:EM_Spectrum_Properties_edit_librsvg.png, edited by author)
The visible light emitted by lamps and LEDs that help us see, and the Wi-Fi® and 5G signals used to transmit data and video, are all forms of EM radiation. The IR portion of the EM spectrum lies between the trailing edge of the red visible light and the start of microwave radiation. The IR spectrum is further divided into three portions, referred to as near-infrared (NIR), mid-infrared (MIR), and far infrared (FIR).
All objects (bodies) emit some form of IR radiation. Emissivity is a measure of how effectively a body or material emits thermal radiation compared to an ideal “black body” that absorbs and emits all radiation that falls upon it. IR emissivity values are between 0 (for a perfect reflector with no emission) and 1 (for a perfect black body). No real material has an emissivity of exactly 1 across all IR wavelengths, but many materials can have high emissivity. For example, certain paints, coatings, and oxidized metals can have an emissivity above 0.9, while human skin has emissivity levels of 0.95 – 0.98, making it almost as good an emitter as a black body (Table 1).
Table 1: Approximate emissivity levels for various materials. A material with high emissivity will absorb and emit radiation more effectively than one with low emissivity. These figures are primarily for infrared wavelengths since emissivity varies with wavelength. (Source: Author)
0.95 - 0.98
0.92 - 0.96
0.90 - 0.95
0.84 - 0.94
0.80 - 0.95
0.03 - 0.10
Emissivity is crucial in temperature measurement, especially with devices that detect and measure thermal IR radiation. Temperature readings can be inaccurate if a material's emissivity is not considered. This is because these instruments assume a certain emissivity level, often close to that of a black body, when, in fact, real-world materials might not emit as efficiently.
There are a myriad of IR sensors and corresponding applications, but focusing on those used to detect and measure thermal IR, we encounter these main types of thermal IR sensors, categorized based on their functionality and application.
A thermopile is a collection of thermocouples connected in series or parallel. A thermocouple consists of two different conductors made from different metals connected at two junctions. A voltage is generated when there's a temperature difference between the junctions, known as the Seebeck effect. In a thermopile sensor, IR radiation from an object strikes the sensor's surface and heats one of the junctions while the other remains cooler. This temperature difference generates a voltage proportional to the amount of infrared radiation striking the sensor.
Thermopile sensors are primarily used for contactless temperature measurements. They can be found in infrared thermometers, ear thermometers, and some HVAC applications—like gas burners—to measure the temperature of objects or surfaces without physical contact.
Passive infrared (PIR) sensors use pyroelectric crystal materials, like lithium tantalate (LiTaO3) or lithium niobate (LiNbO3), to sense IR radiation changes and convert them into electrical signals. These sensors employ a Fresnel lens to focus the IR onto the pyroelectric material, and they have other components for signal processing, such as amplifiers and comparators. PIR sensors passively detect thermal IR radiation from moving objects within their field of view (FoV), typically working within a 2–14µm wavelength range.
As previously stated, humans emit a high amount of IR radiation, which is why PIR sensors are often used in human motion detection systems. When a human (or animal) walks past the sensor, it detects a sudden change in the ambient IR radiation, which triggers the sensor.
The most common use for PIR sensors is motion detection for security alarms. Home automation is another common application of PIR sensors. For instance, a PIR sensor can switch on lights when someone enters a room. They can also be used as energy management devices to turn off unneeded lights or reduce heating and cooling in unoccupied spaces. Another popular application includes wildlife cameras that are triggered to take a picture when an animal passes by.
Since all objects emit IR radiation, the hotter something is, the higher the emissivity. IR imaging sensors convert detected IR radiation into a visible image based on temperature differences. These sensors can visualize temperature variations that are imperceptible to the human eye.
There are two main types of IR imaging sensors:
The primary applications of IR imaging sensors are heat mapping and night vision. For example, IR imaging sensors enable thermal imaging devices used by firefighters to see through smoke, search for people at night, or detect hot spots in fires. The military and law enforcement employ night vision devices for night operations, navigation, and surveillance. Maintenance personnel use thermal imaging to detect overheating components in machines or electrical systems, indicating potential failure points. The medical industry uses medical imaging devices driven by IR imaging sensors for detecting inflammation, poor blood flow, or variations in patient body temperature.
Other IR imaging applications include:
Given all these applications, it’s clear that the ability to visualize temperature differences opens numerous possibilities across a wide range of industries and research areas.
TMOS sensors are advanced presence and motion detection devices ideal for applications like security systems, home automation equipment, and IoT devices. The key advantage of a TMOS sensor is its ability to use thermal transistors to detect both moving and stationary objects. A TMOS sensor’s detection mechanism relies on the thermal MOSFET’s sensitivity to the heating effects of an IR incident on its gate. The gate is thermally insulated using MEMS fabrication. When the incoming IR radiation in the FoV reaches the thermally insulated transistor gate, the IR energy alters the bias of the MOS transistor, enabling the system to sense the temperature of nearby objects or people.
The TMOS operating mode is powered at a sub-threshold voltage below the level needed to turn the transistor fully on. In this mode, the drain-source current is highly sensitive to temperature variations. This sensitivity allows the sensor to detect infrared emissions from humans, regardless of movement.
Unlike conventional PIR sensors, which require motion to produce a measurable response, TMOS sensors can detect stationary objects. Another advantage of TMOS sensors is their simpler construction. PIRs normally require a Fresnel lens to collimate the incoming IR radiation onto the pyroelectric sensor to detect moving objects, but the TMOS sensor allows for lens-free designs.
Also, the TMOS sensor’s integrated design is a huge advantage. The TMOS sensor integrates both the thermal MOSFETs and digital readout circuitry efficiently on the same chip using silicon-on-insulator (SOI) CMOS technology. SOI is crucial for micromachining to thermally isolate the TMOS, ensuring accurate temperature sensing.
Like PIR sensors, TMOS sensors are well-suited for security systems and home automation. But, with their enhanced detection capabilities, TMOS sensors can improve the reliability of security alarms, especially in situations where a person might remain stationary for extended periods. In home automation applications, TMOS sensors can be used in devices to offer more consistent and precise control, depending on human presence. Additionally, the ultra-low-power nature of TMOS sensors can benefit battery-operated IoT devices, making them an ideal choice over the traditional PIR sensors.
This week showcases thermal IR sensors from STMicroelectronics and Broadcom. Both products represent the latest innovation in TMOS technology.
The STMicroelectronics STHS34PF80 is a high-sensitivity IR sensor that is uncooled, factory-calibrated, and has an operating wavelength between 5µm and 20µm. This cutting-edge IR sensing solution is housed in a compact 3.2mm x 4.2mm x 1.455mm 10-lead package. The STHS34PF80 can effortlessly detect human presence up to 6 meters away without a Fresnel lens. Uniquely designed, it zeroes in on an object’s absolute temperature within an 80° FoV and processes the data via its ASIC. Harnessing advanced TMOS technology, it keenly captures even the subtlest temperature shifts, transforming them into precise electrical signals for seamless processing. Plus, with its integrated smart algorithm, it accurately discerns between stationary and moving objects. Additionally, the sensor offers various output data rates (ODRs), ranging from 0.25Hz to 30Hz, has a one-shot mode, and is compatible with SMD mounting. Whether it's for home automation, security systems, or contactless temperature monitoring in the IoT sphere, the STHS34PF80 is a top-tier choice for next-gen thermal imaging applications.
The Broadcom® AFBR-S6EPY eZPyro™ Pyroelectric IR sensor for gas sensing is an advanced IR sensor that is part of the larger eZPyro family, but what makes it special is its ability to sense at wavelengths beyond 5μm. This means it can detect more types of gases, like anesthetics, refrigerants, and exhaust gases. Its high sensitivity and speed help it detect gases quickly and accurately while maintaining its stability and reliability for a long time with little maintenance. The AFBR-S6EPY uses low power but remains highly effective, so devices last longer. Its flexible design allows its settings to be adjusted to fit many IR thermal applications. The device comes with different filters and can work in sync with other sensors using the common I2C connection, making it easy to add to devices. In short, the eZPyro™ SMD+ is a small, efficient, and versatile IR gas sensor.
As the world of thermal infrared sensing evolves, the advancements in TMOS sensor technology represent a significant leap forward in the realm of infrared thermal detection. With its active sensing element, superior temperature sensitivity, lens-free construction, and the ability to detect stationary objects, TMOS sensors offer distinct advantages over traditional PIR sensors. This, combined with the growing demand for efficient, uncooled, cost-effective thermal sensors in the IoT and smart home sectors, positions TMOS as a potential game-changer in contactless temperature measurement applications.
However, while TMOS sensors introduce an exciting frontier in IR sensing, PIR sensors continue to hold a significant place in certain applications with their proven reliability and specific applicability. Balancing the strengths of both TMOS and PIR sensors allows us to harness the best of both worlds in infrared technology.
Avraham, Moshe, Jonathan Nemirovsky, Tanya Blank, Gady Golan, and Yael Nemirovsky. “Toward an Accurate IR Remote Sensing of Body Temperature Radiometer Based on a Novel IR Sensing System Dubbed Digital TMOS.” Micromachines. Multidisciplinary Digital Publishing Institute, April 29, 2022. https://doi.org/10.3390/mi13050703.
Gitelman, Leonid, Zivit Gutman, Sharon Bar-Lev, Sara Stolyarova, Yael Nemirovsky. “TMOS novel uncooled sensors — theory and practice.” 2008 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems, May 13, 2008. https://ieeexplore.ieee.org/document/4562831.
InfraTec. “Infrared Sensor - IR Sensor” n.d. https://www.infratec-infrared.com/sensor-division/service-support/glossary/infrared-sensor/#:~:text=An%20infrared%20sensor%20(IR%20sensor,systems%20to%20detect%20unwelcome%20guests.
Mahadeshwara, Manoj Rajankunte. “Infrared spectroscopy.” Tribonet, October 15, 2022. https://www.tribonet.org/wiki/infrared-spectroscopy/.
Moisello, Elisabetta, Michele Vaiana, Maria Eloisa Castagna, Giuseppe Bruno, Igor Bronk, Tanya Blank, Sharon Bar-Lev, Yael Nemirovsky, Piero Malcovati, Edoardo, Bonizzoni. “Study of a Voltage-Mode Readout Configuration for Micromachined CMOS Transistors for Uncooled IR Sensing.” 2021 IEEE 12th Latin America Symposium on Circuits and Systems (LASCAS), February 21, 2021. https://ieeexplore.ieee.org/document/9459117/citations#citations.
NASA. “The Electromagnetic Spectrum.” March 2013. https://imagine.gsfc.nasa.gov/science/toolbox/emspectrum1.html.
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The adoption of healthcare monitoring devices that are both flexible and conform to the wearer have gained traction1. Spurred on by the need for remote care during the COVID-19 pandemic and technological advances in sensor accuracy, wearable health monitoring devices have become more ubiquitous in many areas of healthcare. In particular, there has been a rise in the development of wearable sensing and monitoring devices for telemedicine operations. Telemedicine has grown significantly in the last few years—and even more so during the COVID-19 pandemic when doctor-patient contact was limited—to remotely monitor patients in their own homes so hospitals could free up much-needed beds. While wearable technologies can be used in clinical environments to provide an analysis, much of their potential is in remote health monitoring.
These wearable sensing devices typically make use of flexible materials, such as polymers, thin films, 2D materials and other nanomaterials. Health monitoring devices often consist of a flexible material that conforms to the wearer and is integrated with electronic components, including sensors, a power source—be it a battery, solar cell, or some other type of energy harvester like a nanogenerator—communication technologies if it transmits data to a central processing system, and any relevant circuitry.
Building electronic components that are small and flexible enough to be used in wearable technologies is not that straightforward, and is why different nanomaterials—especially 2D materials—are often used to create the components because they fit the bill and can be easily integrated. But another approach is gathering interest: printing sensors—the most crucial aspects of a monitoring system—directly onto the wearable device.
These printed sensors are sometimes made of 2D materials and other nanomaterials, but other materials can also be used as well. The ability to print sensors onto wearable health monitoring devices could pave a way to a much simpler and easier route to commercializing more wearable monitoring devices—especially if conventional, inexpensive, or easy-to-use printing technologies are used to print the sensor.
While a range of advanced deposition and nanodeposition methods exist that can fabricate thin-film sensors on the substrate for wearable devices—often a soft polymer due to their flexibility and conformability—a number of biomedical sensors can be printed using screen printing techniques. Other techniques, such as inkjet and transfer printing, can be used as well, but screen printing is seen as the best option for printing sensors.
Screen printing equipment is widely available, has a relatively low cost, and is easier to use than more advanced deposition methods, so it offers a much more scalable and commercially feasible route for using printed sensors in healthcare monitoring devices. Screen printing can also be used with many materials—from polymer solutions to conductive nanomaterial inks—so it is a versatile platform for printing a number of sensors.
So, why print sensors instead of creating them through other manufacturing routes? Screen printing is a simple, fast, and efficient printing technique that can produce a large number of identical patterns in a single print. For large-scale operations and commercialization potential, screen printing can easily be adapted for mass production at a lower cost than other methods.
From a performance perspective, screen printing provides high-resolution patterning and can print over large areas. The ability of screen printing to deposit material on demand in a given location also helps to reduce waste—especially on large scales—compared to traditional manufacturing methods because it is all done in a single step.
There are, therefore, a number of manufacturing benefits with using printing methods. Beyond that, a greater design capability exists for health wearables because printing methods enable the production of sensors that are not only very thin but also provide the ability to print on the surface of the device. This approach is much simpler than trying to integrate devices into the material matrix of the wearable. You can also create a more customized sensor because you can print on demand, and the printing process can be changed to make a different sensor much easier than a typical manufacturing line.
In terms of the materials compatible with screen printing, the scope is vast. Depending on the type of sensor being created, a whole host of materials can be used. On the one hand, a range of metals can be made into printable conductive inks to build the sensor. Common metals for wearable health sensors include gold, copper, platinum, nickel, aluminum, and silver. Beyond metals, a range of nanomaterial composites (graphene, carbon nanotubes composites), functional nanomaterial inks, and silver composites have all been used as the active sensing surface in printed health wearable sensors.
The sensors also need a platform to be printed on. This often takes the form of a conductive polymer so that the sensor can better interact with the skin and the other components to provide higher sensitivity and reliability. PEDOT:PSS is the polymer platform that is most widely used for printed sensors, but other polymer materials include polyacetylene, polypyrrol, polyphenylene, poly (p-phenylene vinylene), and polythiophene polyaniline.
Just as many materials can be used to create printed health sensors, many different types of printable sensors are used in health monitoring devices. Take, for example, strain-based sensors. Strain-based sensors measure a form of movement, and these wearable sensors are used in human-physiological signal monitoring and human-joint motion monitoring approaches.
Another healthcare specialty measures different biomolecules and human signals. Non-strain-based sensors detect the biomolecule of interest directly or by directly measuring a physiological parameter that the patient exhibits. From a molecule-sensing perspective, a number of biomolecules in the blood can be detected, with glucose levels being a common one as well as the presence of sweat on a person’s skin. From a signal detection perspective, printed sensors can now detect respiration and heartbeat levels in a patient and provide remote electrocardiogram (ECG) monitoring.
Another class of printed sensors for wearables is sensor arrays. In sensor arrays, multiple sensors work together to detect more complex issues or issues that require analysis from several stimuli angles. Sensor arrays are helpful in monitoring gait during walking, monitoring the sitting posture of wheelchair users, and monitoring the skin.
Finally, there’s been advances in the development of printed temperature sensors for wearable health devices. Thermal sensing is a key area in disease detection because the body suffers thermal stresses when there is a disease present, and the rise in both skin and deep body temperature can be a primary indicator of a serious disease. Using printed temperature sensors, health wearables can detect a range of chronic diseases, such as cardiovascular, diabetic, and pulmonological diseases, as well as cancer.
Health monitoring wearables are gaining acceptance as an effective way to remotely measure many health factors of a patient. The most important aspect of these wearables is the sensing system that provides the analysis on the patient. For patients to be willing to wear the health monitoring device over an extended amount of time, the sensors and other components need to flex with the wearable component. While integrating flexible sensors is possible—for example, by using thin nanomaterials—printing sensors on the device’s surface is much easier, requires less material, and is much more scalable.
A variety of printed health wearables are already in existence (commercially and academically. The printing methods discussed offer a route to achieve widespread distribution of health wearables. While many options are available, screen printing is the leading printing technology because it is highly versatile and can be used with many materials to create sensors that monitor different aspects of a person’s health—from heart rate to detecting a chance of a disease, to the different biomolecules in their blood, and many more in between.
The Internet of Things (IoT) brings the value of information technologies to the physical world. Through the addition of sensors and actuators to computer networks, we can sense what is happening in the physical world, make those sensor readings available to software algorithms, compute results based upon the readings, and finally drive actuators to use those computations to effect changes back in the physical world. Often, these networks are closed loop, meaning that the physical parameter that an actuator controls is immediately read back into the system by a sensor, closing a continuous real-time loop, and allowing monitoring and tight control of physical processes.
IoT adds actuators to computer networks, and the control of the physical world they provide adds layers of complication to IoT applications. In the past, system problems like software errors, computer security breaches, or component failures could cause significant concern to IT managers because their data could be corrupted, lost, or stolen. When actuators are added to the network—especially if those actuators control powerful or dangerous systems like locomotives, reactors, substations, vehicles, or medical devices—if systems are hacked or suffer failures, serious property damage, personal injury, or even death can result. Therefore, we need to be especially careful with things like system integrity and security if actuators are included.
Most sensors and actuators include types of transducers. Transducers are devices that convert one form of energy to another. For example, the breaks in your car convert mechanical energy to heat energy. For IoT systems, nearly all sensors take some physical parameter and turn it into electrical signals. Similarly, nearly all actuators in IoT systems take electrical signals and convert them into some sort of physical output. The physical parameters used in IoT systems run all over your physics textbook. These could include electrical (voltage, current, power, resistance, capacitance, inductance, frequency, phase, etc.), mechanical (position, speed, acceleration, weight, compass heading, gravity, force, tension, pressure, flow, torque, magnetic field, etc.), acoustic (sound, vibration, seismic, etc.), image (light intensity, cameras, displays, infrared (IR), Light Detection and Ranging (LiDAR, etc.), chemical (potential hydrogen (pH)concentrations, composition, purity, etc.), medical (heart rate, respiration, blood pressure, temperature, electroencephalogram (EEG), etc.) and many more. Literally thousands of types of sensors exist for all these physical parameters, and for most parameters that can be sensed, and analogous actuator exists to modify that parameter in the physical world.
Sensors typically include a raw sensing element or transducer, and a signal processing chain to make the raw readings available to networked computers. Often, the raw sensing element—something like a thermistor, accelerometer, microphone, or light sensor—produces a modest analog signal. This signal must then pass through a signal processing chain that amplifies, filters, and converts the raw signal to a format our control computers and their software can input. This usually involves analog-to-digital conversion and some sort of computer interface like I²C or USB. Sophisticated sensors use digital signal processing techniques to further filter, condition, average, and format the sensor readings in the digital domain.
Actuators have inverse functions. Digital output from the control computers and their software are delivered through an interface to drivers that take the signals and convert them to whatever inputs the transducer in the actuator requires. Often, this consists of a digital-to-analog-converter, an output filter, and some sort of amplifier. Increasingly, digital techniques like digital signal processors (DSPs) and class-D amplifiers are being employed in actuators to make them more accurate, responsive, and energy-efficient.
Often, the number and diversity of sensors and actuators in networks are unexpectedly high. Stop for a minute and consider your smartphone and all the sensor and actuator types it includes. To better illustrate this point, let’s consider a robot system to brew cups of tea. On Star Trek the Next Generation, Captain Pickard would approach his food replicator and say “Tea, Earl Gray, Hot” and in a couple of seconds, it was served. While the transducers needed to materialize a tasty beverage in seconds from pure energy may not exist, at least not yet, let’s consider a practical system that can perform a similar function, and look at all the sensors and actuators a fully realized version could contain:
The first step in this process is to accept the user’s request. Ordering could be via a keypad array (simple push-button sensors), a capacitive touchpad (capacitance sensors), a microphone array (sound sensors), or a camera looking for gestures (image sensors). The orders could also arrive via a computer network using connection types that could include fiber (optical sensors), wireless (RF sensors), or wired (electrical sensors). A local processor accepts this input and coordinates the sensors and actuators in the machine as a real-time control system. If this is a vending machine, additional sensors and actuators accept the currency or credit cards and verify they are genuine (Figure 1).
Figure 1: Image of a beverage brewing machine. (Source: Mouser)
Sensors continuously monitor the temperature and pressure of the brewing water in the reservoir. A resistance heater serves as the actuator that controls the water temperature. If there is a cold water reservoir for cold brew beverages, it has its own temperature and pressure sensors, and perhaps a Peltier module as an actuator to provide thermo-electric cooling. There are feedback loops from the sensors, through the control processor and to the actuators to ensure the water is at the desired temperature for optimal brewing.
Next, we have to select a tea bag. Our machine may have a magazine of tea bags of different types that represent the varieties it can brew. Sensors confirm the presence of the bags—perhaps optically or mechanically. Robot actuators use motors to index the magazine to the right position and lower the selected tea bag into the brew station. Another robot actuator could move the cup into the brew station, and sensors confirm it is positioned correctly.
Now, valve actuators open to let the water into the brew station. Ultrasonic level sensors measure how full the cup is, and cut off the valves at the right moment. Thermal sensors monitor the temperature of the tea as it brews. The motors that lowered the tea bag can gently dip it up and down to facilitate even brewing. An optical sensor can measure the color of the tea to remove the tea bag when the desired strength is achieved. An actuator, like a solenoid, can drop the spent tea bag into the waste receiver.
Additional actuators could add the selected condiments, for example a motor-driven auger to meter sugar or creamer, or a valve to dispense honey. Optical, weight, or ultrasonic sensors in the condiment bins verify the supply is adequate and the dispensers are metering correctly. A little robotic spoon—requiring several motor-driven actuators to position, move, and clean it—gives a final stir and motor actuators open the delivery door and present the cup to the user.
So, using IoT techniques to automate something as simple as brewing a cup of tea may take several dozen sensors and actuators, their interface circuits, a fairly sophisticated processor, and lots of software. I’m not sure making such a machine would be worth it today, but as the capabilities of sensors and actuators continue to increase, and their costs continue to drop, the appearance of such systems is probable.
Sensors are designed to, obviously, sense things. In automotive applications, sensors are developed to help drivers keep up with the health status of their vehicles just by glancing at the instrument panel.
In the early stages of sensor technology, sensors were limited to one part of the car system, such as the fuel gauge (which, in my case, became an ill-advised challenge when the low-fuel light came on).
As these systems developed, the design of modern sensors advanced to function under one centralized system intended for driver convenience. Multiple sensors within these systems can ensure the vehicle’s efficiency. Most of these sensors make sure the vehicle gets the most from its fuel down to its coolants. If these fluids are affected, the engine performance can be influenced.
In modern vehicles, proximity sensors can assist drivers in reverse, humidity sensors can adjust the vehicle's interior temperature, and image sensors aid drivers in monitoring their surroundings.
The emergence of electric vehicles takes sensors to a whole new level. Common types of EV sensors include motor speed, wheel speed, accelerometers, airflow, temperature, and parking aid sensors. Many of these sensors, including those for upcoming vehicle-to-everything (V2X) technology, are developed for improved safety. Other sensors have been developed to address environmental concerns.
In this week's New Tech Tuesday, we'll look at a diverse range of automotive sensors from Murata Electronics, Texas Instruments, and STMicroelectronics designed to improve vehicle life and efficiency.
Texas Instruments TMCS1100/TMCS1100-Q1 Hall-Effect Current Sensors measure DC or AC with high accuracy, excellent linearity, and temperature stability. The sensors' low-drift, temperature-compensated signal chain provides less than 1 percent full-scale error across the device's temperature range. The sensors' inherent galvanic insulation provides a 600V lifetime working voltage with 3kVRMS of galvanic isolation between the current path and circuitry. Applications include motor and load control, power factor correction, overcurrent protection, and DC and AC power monitoring.
Murata Electronics manufactures and supplies sensitive (low-g) acceleration and inclination sensors to the global automotive industry. Murata's line of Automotive Sensors includes both digital and analog accelerometers, gyros, and combined sensors, including 1-axis gyros and 3-axis accelerometers. Murata also offers product development starting with in-house silicon sensor element design. It also has custom ASIC development and application-specific packaging with calibrated and tested sensors.
STMicroelectronics' ASM330LHHX Automotive 6-Axis Inertial Module is AEC-Q100 compliant and features an extended temperature range from -40 to +105°C and has a full-scale acceleration range of ±2/±4/±8/±16g. This system-in-package also features a 3-axis digital accelerometer, a 3-axis digital gyroscope, and is designed to address automotive non-safety applications like dead reckoning, vehicle-to-everything (V2X), telematics, eTolling, anti-theft systems, impact detection, and crash reconstruction.
Sensors have improved the safety aspects and electrical design of vehicles while improving the driving experience. Sensors are guaranteed continued growth as they provide increased benefits to vehicles, including the development of electric vehicles (which leads to autonomous driving) in incalculable ways. Think about that the next time one of your dashboard panel lights flashes.
Sensors are used in many areas of science and everyday society, from monitoring the upstream and downstream processes in a chemical plant to controlling automatic doors, computers, and autonomous vehicles. It is safe to say that sensors are an integral part of everyday life. There’s always a need to improve the accuracy and precision of sensors to provide more reliable data. The need to further optimize sensor accuracy and precision becomes even more important as many areas of manufacturing move toward automated processes supported by the Internet of Things (IoT) and big data as the full-scale implementation of Industry 4.0 approaches.
Because sensors have many different application areas, sensors can measure changes in a localized environment through many different mechanisms. In any case, the design will include an active sensing component to detect changes in the environment. In terms of the mechanisms, some will detect an analyte in the local area through the molecules temporarily binding to the sensors surface—which can be a gaseous molecule (including water for humidity sensing), a liquid, or a specific chemical—whereas some mechanisms rely on a physical deformation of the sensing material—such as stress and strain sensors—and others will rely on an optical or thermal change in the local environment to invoke a detectable response.
One thing that is common throughout all sensing mechanisms is that the sensing mechanism causes a change throughout the sensing material, and this enables the change to be detected and recorded. In many cases, the sensing mechanism causes the electronic properties of the sensing material to change. It is this change that is outputted by the sensor readout in a more usable and readable format. This electrical change can take the form of increasing the conductivity across the sensing material (thus increasing the voltage), or via an increase in the resistivity across the material.
Nanomaterials are inherently thin in nature, and this is a big positive when it comes to sensing applications. In recent years, sensors that use 2D and 1D materials have proven to produce high sensitivities. Because nanomaterials are so thin, their relative surface area is usually high. So, nanomaterials not only make sensors smaller, but they provide a much higher sensing surface area than when bulk materials are used. The higher sensing surface area means that more ‘sensing points’ on the surface are possible compared to other materials. Because the materials are so thin, defects—and specifically charged cavities—can be introduced to the surface of a nanomaterial, and this is a way that nanomaterials can make sensors selective to a certain type of molecule. This can be specific gases—such as ammonia, methane, or water vapor—or specific chemicals within a flowing liquid. In addition, designers can use some surfaces to create defined regions that are specific to one molecule and others that target different molecules. This enables nanomaterial-based sensors to have multi-sensing capabilities.
There is another aspect to their thinness, and that is flexibility. Not all nanomaterials are flexible, but those that are—such as graphene—can be deformed by a large degree without breaking, and this again changes the electrical conductivity across the nanomaterial (which is detected). Many flexible nanomaterials also have a high tensile strength—just look at graphene with the highest known tensile strength of any single material. Therefore, the flexibility of some nanomaterials can become a sensing mechanism with the ability to return to its original conformation and have a long useful life. In many cases, nanomaterials can also behave the same under pressure and provide a detectable response. There are various piezoelectric and piezoresistive nanomaterials that will deform under a strain and invoke a change in their electrical current—much like bulk piezoelectric and piezoresistive material but on a much smaller scale—which makes them more accurate to small strain deformations.
Some nanomaterials are also thermally conductive and can be exposed to high amounts of heat, which is an ideal property for temperature sensors. In these instances, when the local temperature is increased, it can be detected by the drop in thermal resistivity across the nanomaterial.
Another property that is beneficial from some nanomaterials is their optical properties. Some nanomaterials possess photo-absorption properties, which when coupled with a high electrical conductivity and charge carrier mobility, can act as highly sensitive photodetectors. In some cases, this can extend beyond visible light into other areas of the electromagnetic spectrum, such as for UV radiation.
We’ve talked about how the different mechanisms and properties of nanomaterials help to induce a change in the electrical conductivity of the nanomaterial and/or other sensing surfaces. But, the electrical conductivity and charge carrier mobility—the ability for charged particles such as electrons and holes to move through the atomic lattice—are two properties in themselves that many nanomaterials excel at. Many nanomaterials possess highly conducting or semiconducting electronic properties, which alongside a high-charge carrier mobility, makes the electrical change across the nanomaterial significantly more sensitive through being considerably more responsive to minor changes.
In the case of those nanomaterials that exhibit semiconducting properties, they can be used to detect molecules that have both acceptor and donor electronic properties. Semiconducting nanomaterials can employ mechanisms that cause holes to deplete from the valence band—thus increasing the resistivity across the nanomaterial—or mechanisms which cause electrons to migrate to the conduction band—thus increasing the conductivity. Both mechanisms are easily detectable via the change in applied voltage across the nanomaterial.
We’ve talked above about nanomaterials on their own, but designers can incorporate many nanomaterials into hybrid materials (such as composites) and bring about benefits in this form. When they are incorporated into a hybrid matrix, the nanomaterial will bind intermolecularly with the other materials. Intermolecular bonding can be through hydrogen bonding (if the nanomaterial contains polar groups), van der Waals forces, and π-π stacking. These intermolecular interactions enable efficient charge transfer mechanisms to take place where there are delocalized electrons (particularly where π-electron networks are formed) within the hybrid material. This provides a more efficient conduction mechanism compared to when they’re not included in the matrix, which results in a higher degree of sensitivity.
Not all nanomaterials are suitable for sensing applications, but those that are can provide a significant improvement in the sensing capabilities of a sensor over other materials. Overall, there are a range of beneficial properties—from a high surface area to thermal conductivity, a high electrical conductivity, and charge transfer properties—that designers can use to provide more accurate sensing mechanisms over other sensing materials.
There are many different areas where sensors use nanomaterials, and these include, but are not limited to, stress/strain gauges, various types of biosensors, temperature and humidity sensors, pressure sensors, optical sensors, capacitance sensors, piezoelectric sensors, and piezoresistive sensors.
(Source: anetlanda - stock.adobe.com)
We depend on our senses to help us understand the world around us. Each sense contributes specific information that our brains combine to create a picture of our environment.
With the growth of artificial intelligence (AI) and machine learning (ML), we are increasingly dependent on technology to help make complex decisions on our behalf. Machines powered by AI and ML must also form an accurate picture of their surroundings, and we should equip them with the technology they require to collect the information needed.
Sensors play a vital role in this modern technology by providing machines with the information they need to function correctly. Designers have long sought to provide machines with equivalents to the senses. The human brain is superbly conditioned to understand the information that the sensory organs can collect. However, artificial sensors often need more sophistication. Early sensors could not interpret the information gathered without the processing power that is now available.
Many sensory devices, including light and proximity sensors, are limited because they rely on a clear line of sight or physical contact to function correctly. As the applications for today's technology become more complex, designers can no longer rely on simple sensing technology.
The sense of smell, known as olfaction, is a form of chemical analysis of low concentrations of molecules that are suspended in the air. When these molecules encounter a receptor in the nose, signals are transmitted to the parts of the brain responsible for smell identification. Olfaction sensitivity depends on the concentration of receptors, which varies from species to species. For example, a dog’s nose is far more sensitive than that of a human, and they can identify concentrations of chemicals that are far too weak for humans to notice.
Detection dogs have been valuable companions to humans in various tasks. Aside from the search for contraband or weapons, these dogs can also help detect medical conditions before symptoms develop. They have been used in other fields too, including environmental management and fire investigation. However, the training of a detection dog takes many months, and they are often only trained for a certain number of scents. Dogs are also of limited value in an industrial environment.
Olfactory sensors offer versatile and unique advantages as a detection method. Unlike image recognition and other vision-based technologies, olfaction does not rely on line-of-sight detection. Odors can be detected from unseen objects, whether hidden, obscured, or simply not visible by other means, allowing olfactory sensor technology to work without the need for invasive procedures. This makes the latest advances in olfactory sensors ideally suited for a range of applications.
With advances in technology, artificial smell sensors, designed to mimic this extraordinary human ability, are now finding applications in diverse areas. By analyzing chemical signatures in the air, these sensors are unlocking new levels of safety, efficiency, and early detection in places like airport security, factory floors, and doctors' offices.
The sense of smell does not require physical contact, making it ideal for detection in large spaces. For example, olfactory sensors can be deployed in airport security, collecting information about passengers or bags as they pass. Equipped with a database containing chemical signatures and the processing power to analyze a very high number of samples in real-time, these sensors allow security personnel to let passengers flow through the facility easily, only stopping those identified as of particular interest.
The industrial world is also adopting smell sensors. Many industrial processes have the potential to create hazardous byproducts. Olfactory sensors can monitor air conditions and highlight the dangerous buildup of harmful chemicals. They can also deliver meaningful information about the industrial process itself. High concentrations of unburnt fuel in the atmosphere can result from incomplete combustion, indicating that a process is using energy inefficiently. A different smell could indicate if oxidation requires prevention. In both cases, olfactory sensors can provide an early warning of a problem and advise the best course of action to remedy the situation without human intervention when combined with the latest AI technology.
The healthcare industry is home to some of the most exciting applications for olfactory sensors. Medical technology depends on early diagnosis to deliver the best clinical outcome for patients. Many conditions, from cancer to diabetes, cause detectable changes in the body's chemistry. Using sensors to detect changing odors can provide a critical early diagnosis that will significantly improve the chances of effective treatment and recovery. These sensors' non-contact, non-invasive nature makes them useful for an initial consultation without delay caused by more traditional blood or tissue analysis techniques.
Alongside traditional vision-based sensors, olfactory sensors deliver a range of advantages over other technologies. They do not require a physical line of sight, nor do they need physical contact to work.
With applications in a wide range of industries and applications, from security and industry to groundbreaking medicine, olfactory sensors work in conjunction with other techniques to provide machine systems with the feedback they need to help improve lives.
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