Tag Archives: sensors

Position and Distance Sensors for Accurate Tracking

Many engineering processes like security systems, feedback control, and robotics rely on position and distance sensors for machines to operate safely and accurately. These sensors provide vital information in real-time about the position and displacement of an object. The coordinates of an object relative to a known reference give a measure of its position. The movement of the object from one location to another with a determined angle and distance, is its displacement.

The history of position sensors begins with the potentiometer. Johann Poggendorf invented the potentiometer in 1841. With a change in resistance, it measures the position of a movable contact on a resistive track. Later, the field of position sensing was taken over by magnetoresistive sensors, which measured the change in a material’s resistance due to a magnetic field.

Soon, new position sensors appeared. These were based on solid-state electronics and included LVDTs or Linear Variable Differential Transformers. Later came digital position sensors, which offered high-resolution measurements suitable for integrating into computer systems. The latest trend is to miniaturize position and distance sensors.

Position and distance sensors provide real-time information by detecting changes in physical properties like magnetic field, inductance, capacitance, and displacement. There are various position and distance sensors, including level, thickness, radar, capacitive, gravitational, and potentiometric sensors.

Thickness and level sensors are useful in measuring the thickness and level of powders, solids, and liquids. Primary technologies they employ include optical, laser, and ultrasonic. For instance, they calculate the thickness of a material based on distance measurement between an object and the sensor within a referenced space. In the same way, level sensors measure the height of the liquid in a container to produce a level reading. Industries using thickness and level sensors include manufacturing, pharmaceutical, chemical, and food processing.

RADAR or Radio Detection and Ranging technology locates objects using radio waves. They detect objects by transmitting radio waves at specific frequencies and listening for echoes of the signals as they bounce back from objects. By analyzing the time and frequency of the returned signal, radar can determine the object’s size, speed, and location.

Capacitive sensors measure distance by measuring the change in capacitance due to the proximity of objects to the sensor. The basic sensor has two conductive plates with a dielectric material separating them. As an object moves closer to one of the plates, the capacitance of the sensor changes. This generates a voltage signal proportional to the distance of the object from the plate.

The advantage of capacitive sensors is their formidable accuracy and resolution. For instance, capacitive sensors can measure displacement in the nanometric range. Harsh environments do not affect these sensors. However, outside interference in the form of humidity and magnetic fields can affect their performance.

Potentiometric sensors typically measure linear or angular displacement. For this, the sensor employs a resistive element. The basic construction consists of a thin resistive wire of film wound around an insulating element like a ceramic rod. A metallic wiper moves on the resistive element. As the wiper moves, its resistance changes with reference to one end of the resistive wire. An electronic circuit quantifies the resistance to produce a voltage output, indicating the displacement of the wiper attached to the measured object.

Sensors at the Heart of IoT

IoT, or the Internet of Things, depends on sensors. So much so, there would not be any IoT, IIoT, or for that matter, any type of Industry 4.0, at all, without sensors. As the same factors apply to all the three, we will use IoT as a simplification. However, some basic definitions first.

As a simple, general definition, IoT involves devices intercommunicating with useful information. As their names suggest, for IIoT and Industry 4.0, these devices are mainly located in factories. While IIoT is a network of interconnected devices and machines on a plant floor, Industry 4.0 goes a step further. Apart from incorporating IIoT, Industry 4.0 expands on the network, including higher level systems as well. This allows Industry 4.0 to process and analyze data from IIoT, while using it for a wider array of functions, including looping it back into the network for control.

However, the entire network has sensors as its basis, supplying it with the necessary raw data. Typically, the output from sensors is in the form of electrical analog signals, and IoT creates the fundamental distinction between data and information.

This distinction is easier to explain with an example. For instance, a temperature sensor, say, a thermistor, shows electrical resistance that varies with temperature. However, that resistance is in the form of raw data, in ohms. It has no meaning to us, until we are able to correlate it to degrees.

Typically, we measure the resistance with a bridge circuit, effectively converting the resistance to voltage. Next, we apply the derived voltage to a measuring equipment that we have calibrated to show voltage as degrees. This way, we have effectively converted data into information useful to us, humans. However, we can still use the derived voltage to control an electric heater or inform a predictive maintenance system of the temperature of a motor.

But information, once we have derived it from raw data, has almost endless uses. This is the realm of IoT, intercommunicating useful information among devices.

To be useful for IoT, we must convert the analog data from a sensor to a digital form. Typically, the electronics required for doing this is the ADC or Analog to Digital Converter. With IoT applications growing rapidly, users are also speeding up their networks, thereby handling even larger amounts of data, making them more power efficient.

Scientists have evolved a new method for handling large amounts of data that does not require the IoT devices to have large amounts of memory. The devices send their data over the internet to external data centers, the cloud. There, other computers handle the proper storing and analysis of the data. However, this requires higher bandwidth and involves latency.

This is where the smart sensor makes its entry. Smart sensors share the workload. A sensor is deemed smart when it is embedded within a package that has electronics for preprocessing, such as for signal conditioning, analog to digital conversion, and wireless transmission of the data. Lately, smart sensors are also incorporating AI or Artificial Intelligence capabilities.

IoT Sensor Design

Individuals are progressively integrating electrical components into nearly every system possible, thereby imbibing these systems with a degree of intelligence. Nevertheless, to meet the intelligence requirements posed by diverse business applications, especially in healthcare, consumer settings, industrial sectors, and within building environments, there is a growing necessity to incorporate a multitude of sensors.

These sensors now have a common name—IoT or Internet of Things sensors. Typically, these must be of a diverse variety, especially if they are to minimize errors and enhance insights. As sensors gather data through sensor fusion, users build ML or Machine Learning algorithms and AI or Artificial Intelligence around sensor fusion concepts. They do this for many modern applications, which include advanced driver safety and autonomous driving, industrial and worker safety, security, and audience insights.

Other capabilities are also emerging. These include TSN or time-sensitive networking, with high-reliability, low-latency, and network determinism features. These are evident in the latest wireless communication devices conforming to modern standards for Wi-Fi and 5G. To implement these capabilities, it is necessary that sensor modules have ultra-low latency at high Throughput. Without reliable sensor data, it is practically impossible to implement these features.

Turning any sensor into an IoT sensor requires effectively digitizing its output while deploying the sensor alongside communication hardware and placing the combination in a location suitable for gathering useful data. This is the typical use case for sensors in an industrial location, suitable for radar, proximity sensors, and load sensors. In fact, sensors are now tracking assets like autonomous mobile robots working in facilities.

IoT system developers and sensor integrators are under increasing pressure to reduce integration errors through additional processing circuits. Another growing concern is sensor latency. Users are demanding high-resolution data accurate to 100s of nanoseconds, especially in proximity sensor technologies following the high growth of autonomous vehicles and automated robotics.

Such new factors are leading to additional considerations in IoT sensor design. Two key trends in the design of sensors are footprint reduction and enhancing their fusion capabilities. As a result, designers are integrating multiple sensors within a single chip. This is a shift towards a new technology known as SoC or system-on-chip.

Manufacturers are also using MEMS technology for fabricating sensors for position and inertial measurements such as those that gyroscopes and accelerometers use. Although the MEMS technology has the advantage of fabrication in a semiconductor process alongside digital circuits, there are sensors where this technology is not viable.

Magnetic sensors, high-frequency sensors, and others need to use ferromagnetic materials, metastructures, or other exotic semiconductors. Manufacturers are investing substantially towards the development of these sensor technologies using SiP or system-in-package modules with 2D or 2.5D structures, to optimize them for use in constrained spaces and to integrate them to reduce delays.

Considerations for modern sensor design also include efforts to reduce intrinsic errors that affect many sensor types like piezoelectric sensors. Such sensors are often prone to RF interference, magnetic interference, electrical interference, oscillations, vibration, and shock. Designers mitigate the effect of intrinsic errors through additional processing like averaging and windowing.

The above trends are only the tip of the iceberg. There are many other factors influencing the growing sensor design complexity and the need to accommodate better features.

Ultrasonic Sensors in IoT

For sensing, it has been a standard practice to employ ultrasonic sensors. This is mainly due to their exceptional capabilities, low cost, and flexibility. With IoT or the Internet of Things now virtually entering most industries and markets, one can now find ultrasonic sensors in newer applications in healthcare, industrial, and smart offices and homes.

As their name suggests, ultrasonic sensors function using sound waves, especially those beyond the hearing capability of humans. These sensors typically send out chirps or small bursts of sound in the range of 23 kHz to 40 kHz. As these chirps bounce back from nearby objects, the sensor detects them. It keeps track of the time taken by the chirp for a round trip and thereby calculates the distance to the object based on the speed of sound.

There are several benefits from using ultrasonic sensors, the major one being very accurate detection of the object. The effect of material is also minimal—the sensor uses sound waves and not electromagnetic waves—the transparency or color of the object has minimum effect on the readings. Additionally, this also means that apart from detecting solid objects, ultrasonic sensors are equally good at detecting gases and liquid levels.

As ultrasonic sensors do not depend on or produce light during their operation, they are well-suited for applications that use variable light conditions. With their relatively small footprints, low cost, and high refresh rates, ultrasonic sensors are well-established over other technologies, like inductive, laser, and photoelectric sensors.

According to a recent study, the smart-office market will likely reach US$90 billion by 2030. This is mainly due to a surging demand for sensor-based networks, brought about by the need for safety and advancements in technology. Ultrasonic sensors will be playing an expanded role due to industry and local regulations supporting increased energy efficiency for automating different processes around the office.

A prime example of this is lighting and HVAC control in offices. Ultrasonic sensors are adept at detecting populated rooms in offices all through the day. This data is useful in programming HVAC systems, for keeping rooms hot or cool when populated, and turning the system off at the end of the day, kicking back on at first arrival.

Similarly, as people enter or leave rooms or areas of the office, ultrasonic sensors can control the lights automatically. Although the process looks simple, the energy savings from cutting back on lighting and HVAC can be huge. This is especially so for large office buildings that can have many unoccupied office spaces. For sensing objects across large areas, ultrasonic sensors offer ideal solutions, with detecting ranges of 15+ meters and detecting beam angles of >80°.

Additionally, smart offices can also have other smart applications like hygiene and touchless building entry devices. Touchless devices include automatic door entries and touchless hygiene products include faucets, soap dispensers, paper towel dispensers, and automatically lifting waste bin lids. During the COVID-19 pandemic, people’s awareness of these common applications has increased as public health and safety became critical for local offices and businesses.

FIR Temperature Sensor

Among the many things that the COVID-19 pandemic taught us was the technique of assessing the human body temperature non-invasively. This was used in several locations, including hospitals, schools, and airports, employing an infrared sensor for measuring the surface temperature without making physical contact. Now, this is a popular method used commonly for taking body temperature. While being non-invasive, infrared thermometers also provide quick and reliable readings.

The accuracy of the infrared thermometer technique was affected by variables including the nature of the surface under measurement and its surroundings. However, scientists have largely resolved these issues, attaining medical-grade accuracy and compensation. In the process, they have also successfully lowered the size of the thermometer. Accordingly, Melexis Microelectronic Integrated Systems have developed a miniature infrared temperature sensor.

Based in Belgium, Melexis specializes in ICs and microelectronic sensors for applications involving consumer, automotive, digital health, smart devices, and energy management. For instance, Samsung is using one of Melexis’s products, the MLX90832 temperature sensor that works on FIR or far-infrared technology, for their GW5 smartwatch. The medical-grade version of the Melexis temperature sensor allows menstrual cycle tracking. Such continuous but reliable temperature monitoring opens up a vast range of newer applications in health, sports, and other domains.

The FIR sensor from Melexis is an SMD or surface-mount device that can accurately measure an object’s infrared radiation to record its temperature. The SMD packaging makes the sensor suitable for a large variety of applications, such as wearables, including hearables or in-ear devices, and point-of-care clinical applications that require highly accurate human-body temperature measurement.

Non-contact temperature measurement has several advantages over the more traditional contact methods. It can be helpful in several circumstances where making physical contact is undesirable, such as when the object is fragile, located in a dangerous area, or moving. It is helpful when a quick response is desirable, or when it is not possible to guarantee an excellent thermal contact between the object under test and the sensor. Moreover, the technique of measuring temperature without contact can be more accurate and yield results that are more reliable than contact temperature measurement methods.

The extremely small 3 x 3 x 1 mm3 QFN package of the Melexis MLX90832 is a full-solution device that incorporates the optics, a sensor element, digital signal processing, and digital interfacing, providing a quick and simple integration for a wide range of modern applications within a limited space.

With factory calibration, the MLX90832 offers high accuracy, while Melexis has ensured thermal and electrical precautions internally so that the device has adequate compensation when operating in thermally harsh external conditions. Internally, the voltage signal from the thermopile element undergoes amplification and digitization. After undergoing digital filtering, the raw measurement data resides in the RAM of the device. All the functions remain under the control of a state machine. An I2C interface makes available the results of each measurement conversion, while allowing access to the control registers of the internal state machines, the RAM for auxiliary measurement data and pixel readings, and the E2PROM for calibration constants, the trimming values, and other measurement/device settings.

Modern RTD-Based Sensors

The popular belief is to not fix things that aren’t broken. The idea is to not tamper with something performing reliably and proving its worth. This advice aptly applies to circuit designs using RTD sensors that efficiently and quietly measure temperature in industrial manufacturing facilities worldwide.

However, in meeting the requirements of Industry 4.0, where smart factories are the norm, it is now evident that the current RTD sensors in use are not fitting the purpose. Automation engineers today want industrial temperature sensors to be of smaller form factors, flexible with communications, and capable of remote reconfigurability. Incumbent solutions, sadly, are unable to support them. However, it is possible to easily redesign these sensors to equip them with the necessary features to meet the new industrial design.

The RTD industrial temperature sensor translates temperature, a physical quantity, into an electrical signal. The typical range of such sensors is between -200 °C and +850 °C, with a highly linear response across it. RTDs commonly use metal elements like copper, nickel, and platinum. Among these, PT1000 and PT100 platinum RTDs are the most popular. While an RTD can use either two, three, or four wires, the 3-wire and 4-wire versions are the most popular. Being passive devices, RTDs require an excitation current for producing an output voltage. A voltage reference generates this current, with an operational amplifier acting as a buffer for driving the current into the RTD, which produces an output voltage signal varying in response to changes in temperature. The voltage signal may vary from tens to hundreds of millivolts depending on the type of RTD in use and the measured temperature.

An AFE or Analog Front End conditions and amplifies the low amplitude voltage signal from the RTD before the ADC or Analog to Digital converter digitizes it. A microcontroller runs an algorithm over the digitized signal, compensating for any non-linearity in it. The microcontroller then sends the processed digital output to a communications interface for transmission to a process controller. A typical implementation of the AFE is by a signal chain of components with each performing a dedicated function.

This discrete approach requires a large PCB or printed circuit board for accommodating all the ICs and power and signal routing, setting a minimum size for the sensor enclosure. Rather, modern RTD-based sensors use a superior and more minimal approach—the AD7124-4, an integrated AFE.

The AD7124-4 is a compact IC in a single package. It includes a multiplexer for accommodating multiple-wire RTDs, a voltage reference, a programmable gain amplifier, and an ADC using the sigma-delta operating principles. The IC has the capability to provide the necessary excitation currents for the RTD. The entire arrangement effectively replaces five of the signal-chain components from the traditional setup. Not only does this significantly reduce the amount of board space necessary, but it also enables the sensor to use a much smaller enclosure.

Next comes the communications interface. Modern RTD-based sensors typically use the IO-Link which eliminates the use of expensive ASICs for implementing specific network protocols. IO-Link is a 3-wire industrial communications standard for linking sensors and actuators with all industrial control networks.

In-Circuit Monitors for Electronic Devices

During a chip’s lifetime, there can be a wide variety of issues cropping up. Engineers are using sensors that can address them. As the semiconductor ecosystem touches a wide application space, sensors, and in-circuit monitors are playing an increasing role in managing the silicon lifecycle, thereby improving its resiliency and reliability.

Engineers are expecting a drastic improvement in the reliability of electronic devices with the addition of these sensors and in-circuit monitors. These expectations are due to a combination of sensor placements in true system-level design, in- and on-chip monitors, and an improvement in data analysis.

In the future, with engineers placing more monitors and sensors at strategic locations for collecting data, the combination, and analysis of this data is likely to increase tremendously. In addition, this will lead to a much more detailed understanding of what goes wrong in real time in the life of a semiconductor. Important to note, this is likely to open the door to recovery schemes for keeping devices functioning until they are due for replacement or repair.

All of the above depends on the complexity of the product. Although some regulatory standards for miniaturization are under study, the complexity of the product drives the use of sensors and in-circuit monitors. With consumers wanting greater capabilities in their hands, the requirement is going to increase substantially.

Although users were not interested earlier in concepts like resilience, predictability, and observability, things are changing fast. Chip architects are paying more attention to how systems and devices behave over time, including issues such as silent data corruption. Where earlier, it was hard to articulate the business reasons for such inclusion, chip architects are realizing there are missing pieces. While it is still a tussle between the why and how much, the realization is dawning that it is impossible to have all the computing resources or complex monitors-on-chip that can tackle all scenarios. Especially when such additions need real estate and power to function.

Designers are beginning to realize that advanced design techniques, in conjunction with manufacturing complexities and the latest process nodes, are leading to new challenges. These challenges appear as variable power consumption and affect the useful life of the semiconductor. The power consumption pattern and performance characteristics of a chip change as it travels along the silicon value chain. The variation starts with the pre-silicon design, moving on to new-product bring-up, to system integration, and finally, to its in-field usage.

Monitoring the way a chip degrades over time, can throw light on many types of semiconductor failures, especially with BTI or bias temperature instability. Using in-circuit monitors, it is now possible to measure areas that show performance and power degradation, on-die temperature variations, and workload stress, and monitor die-to-die interconnects for heterogeneous designs. Mission-critical systems define specifications such as safety and reliability as the key differentiating parameters. Moreover, with device functionality degrading over time, it is necessary to evolve tests that include lifetime operation as well.

The industry is now widely adopting an approach that includes more and more sensors and in-circuit monitors for electronic devices to monitor the most prominent slack paths. 

Cooling Machine Vision with Peltier Solutions

The industry is using machine vision for replacing manual examination, assessment, and human decision-making. For this, they are using video hardware supplemented with software systems. The technology is highly effective for inspection, quality control, wire bonding, robotics, and down-the-hole applications. Machine vision systems obtain their information by analyzing the images of specific processes or activities.

Apart from inspection systems, the industry also uses machine vision for the sophisticated detection of objects and for recognizing them. Machine vision is irreplaceable in collision avoidance systems that the next generation of autonomous vehicles, robotics, and drones are using. Recently, scientists are using machine vision in many machine learning and artificial intelligence systems, such as facial recognition.

However, for all the above to be successful, the first requirement is the machine vision must be capable of capturing images of high quality. For this, machine vision systems employ image sensors and cameras that are temperature sensitive. They require active cooling for delivering optimal image resolutions that are independent of the operating environment.

Typically, machine vision applications make use of two types of sensors—CCD or charge-coupled devices, and CMOS or complementary metal-oxide semiconductor sensors. For both, the basic functionality is to convert photons to electrons that are necessary for digital processing. Both types of sensors are sensitive to temperature, as thermal noise affects their image resolution, and thermal noise increases with the rising temperature of the sensor assembly. This depends on environmental conditions or the heat generated by the surrounding electronics, which can raise the temperature of the sensor beyond its maximum operating specification.

By rough estimation, the dark current of a sensor doubles for every 6 °C rise in temperature. By dropping the temperature by 20 °C, it is possible to reduce the noise floor by 10 dB, effectively improving the dynamic range by the same figure. When operating outdoors, the effect is more pronounced, as the temperature can easily exceed 40 °C. Solid-state Peltier coolers can prevent image quality deterioration, by reducing and maintaining the temperature of the sensor to below its maximum operating temperature, thereby helping to obtain high image resolution.

However, it is a challenge to spot cool CCD and CMOS sensors in machine vision system applications. Adding a Peltier cooling device increases the size, cost, and weight. It also adds to the complexity of the imaging system. Cooling of imaging sensors can lead to condensation on surfaces exposed to temperatures below the dew point. That is why vision systems are mainly contained within a vacuum environment that has insulated surfaces on the exterior. This prevents the build-up of condensation over time.

The temperature in the 50-60 °C range primarily affects the image quality of CCD and CMOS sensors. However, this depends on the quality of the sensor as well. For sensors in indoor applications just above ambient, a free convection heat sink with good airflow may be adequate to cool a CMOS sensor. However, this passive thermal solution may not suffice for outdoor applications. Active cooling with a Peltier cooling solution is the only option here.

New Graphene Sensors

While more advanced technology sectors have been late in adopting graphene, it finds plenty of interest in both lower- and high-tech applications. One of these applications is sensors based on graphene. Different industry sectors have steadily been using these sensors.

This is because graphene can be the basis of an effective sensing platform. Several interesting applications manifest this in many ways. Of these, the biosensor subsector is especially notable in attracting heavy investment. This trend is likely to continue even beyond 2022.

With graphene properties being exhaustively documented, many are now aware that they can do a lot with graphene and that many applications can benefit from its properties. Although many of these aspects are often subject to some hype, the fundamental properties of graphene make it a superior material of choice. This is primarily of account of graphene being suitable as an active sensing surface in many sensing applications.

The major advantage of graphene is its inherent thinness. This allows sensing devices made from graphene to be far more flexible and smaller in comparison to many other materials. In addition, graphene forms a very high-end active surface area.

In applications involving sensing, a high surface area is beneficial as it allows interaction with a larger range of molecules like different gases, water, biomolecules, and many other molecular stimuli. With graphene being an active surface, it is possible to attach a number of different molecular receptors and molecules to a sheet of graphene. This helps to create sensors that can detect specific molecules.

However, graphene has more advantages. Because of the high electrical conductivity of graphene, its high charge transfer properties, and high charge carrier mobility, sensors made from graphene exhibit very high sensitivity. That means, graphene sensors will generate a detectable response even from a small interaction with the environment. This happens because the excellent properties of graphene help in changing the resistivity across the graphene sheet with each small interaction. Therefore, graphene sensor help to detect even the smallest amounts of stimuli from the environment.

Because of their innate thinness, it is possible to make graphene-based sensors in small form factors, while retaining their highly sensitive sensing characteristics. It is also possible to tailor the sensors chemically for detecting a range of stimuli from the environment. This characteristic has led to the generation of much commercial interest in developing various graphene-based sensors for a variety of commercial markets involving many applications.

For instance, Paragraf has a graphene-based Hall-effect sensor that can measure changes in a magnetic field using the Hall effect. Therefore, this has increased the possibility of adding many new and interesting application areas to those that graphene sensors had not ventured into so far.

In the past year, Paragraf has demonstrated that Hall-effect sensors based on graphene are highly sensitive. They can measure currents flowing in batteries within electric vehicles for monitoring their status. Paragraf makes these sensors by depositing single layers of contamination-free graphene directly on a wafer. They repeat this following standard semiconductor manufacturing processes. This has allowed them to make several volume applications possible now, including those for fast and sensitive biosensors for detecting biomarkers within liquid samples.

Haptic Skin Sensors

Although great technological advances are taking place to engage our eyes and ears in the virtual worlds, engaging other senses like touch is a different ballgame altogether. At City University in Hong Kong, engineers have developed a wearable, thin electronic skin called WeTac. It offers tactile feedback in AR and VR.

At present, there are several wearable devices with designs that allow users to manipulate virtual objects while receiving haptic feedback from them. However, not only are these devices heavy and big but also require tangles of wire and complex setups.

In contrast, the WeTac system is one of the neatest arrangements among all others. The engineers have made it from a rubbery hydrogel that makes it stick to the palm and on the front of the fingers. The device connects to a small battery and has a Bluetooth communications system that sits on the forearm in a 5-square-centimeter patch. The user can recharge the battery wirelessly.

The hydrogel has 32 electrodes embedded in it. The electrodes are spread out all over the palm, the thumb, and the fingers. The system sends electrical currents through these electrodes to produce tactile sensations.

According to the WeTac team, they can stimulate a specific combination of these electrodes at varying strengths. This allows them to simulate a wide range of experiences. They have demonstrated this by simulating catching a tennis ball or generating the feel of a virtual mouse moving across the hand. They claim they can ramp up the sensation to uncomfortable levels, but not to the extent of making them painful. This can give negative feedback, such as a reaction to touching a digital cactus.

According to the researchers, they can pair the system up with either augmented or virtual reality. They can thus simulate some intriguing use cases. For instance, it is possible to feel the rhythm of slicing through VR blocks in Beat Saber, or catch Pokemon while petting a Pikachu in the park in AR.

Using the WeTac system, it may be possible to control robots remotely or transmit to the human operator the tactile sensations of the robot as it grips something.

Syntouch has a new tactile sensor that performs three important functions. First, it measures the impedance using a flexible bladder placed against an array of sensing electrodes fixed in a rigid core. This arrangement helps to measure deformity, somewhat like the human finger, using its ductile skin and flesh against the rigid bone structure inside it. The finger uses its fingernails to cause bulges in the skin for detecting shear forces.

Second, the tactile sensor registers micro-vibrations using a pressure sensor that the sensor core has mounted on its inside. This enables measurements of surface texture and roughness. The fingerprints are very crucial here, as they can interact with the texture.

Third, the sensor has a thermistor. Its electrical resistance is a function of temperature. Just like the human finger can sense heat, the sensor also generates heat, while the thermistor allows it to detect how it exchanges this heat when the finger touches an object.