Sustainable Medical Wearables

Most of us use fitness and medical wearables today. These amazing devices can sustain the rigors of everyday life. A fall to the floor or a drop of liquid does not keep these devices from working or fulfilling their purpose.

Whether consumers use them for everyday purposes, or diagnostic testing requires using them for limited use, medical wearables must be capable of withstanding general wear and tear, disinfecting, and cleaning. Multiple patients may use the same medical wearable In the course of their lifetime. So, if they are to last, they must be capable of inherently protecting themselves from contaminants and liquids, radiation, and impact from hard objects and surfaces.

For many people, a wearable is either a FitBit or an Apple Watch. However, apart from these popular consumer wearables, there are several other small medical devices that are necessary for evaluating patients and monitoring them for short- or long-term, such as for heart-related disorders like cardiac arrhythmias.

Transdermal patches are wearable devices that deliver extended-release medication. Typically, patients wear them for long periods, requiring them to balance breathability with adhesive hold, while being comfortable for the wearer. It is also necessary that the materials in the device do not interact negatively with the pharmaceuticals and medicines that the device will be delivered to the wearer.

Nowadays, it is common to find microfluidic diagnostic devices such as for diabetic testing with blood glucose strips. These track biomarkers like glucose and pH levels at molecular levels of sweat, blood, and other fluids. These small and intricate devices with sensors typically collect data from the wearer. Such devices contain printed flex circuits, sensors, electrodes, and batteries.

There is a broad category known as wearable biometric monitoring devices for tracking biometric markers. These markers include parameters like heart rate, temperature, movement, and respiration, among many others. These are devices like blood pressure monitors, continuous glucose monitors, and sleep trackers. Apart from the need for these devices to stick to the user with adhesives, they possess the functionality and the ability to wirelessly transmit information that it collects. Apart from the standard internal components like flex circuits, sensors, electrodes, and batteries, these devices also contain devices and circuits for wireless transmission and reception.

Medical wearables typically contain critical components like sealing gaskets. These are necessary not only for keeping out unwanted contaminants, but they must also be safe for contact with the human body and skin—depending on where they are located in use. Manufacturers use 3D printers for fabricating orthotics and prosthetics, and they use fireproof sealing gaskets. However, sealing gaskets used in medical wearables are made of different materials, as they must come in contact with bodily fluids, human tissue, drugs, and medical fluids.

May requirements guide the selection of materials for medical wearables. For instance, sealing gaskets may need to conduct electricity, be flame-resistant, and at the same time, be protective against electrostatic discharge. Typically, they belong to a wide spectrum of elastomers and polymers. Whatever the material used, it must be durable. For medical wearables, it is essential they consider how people live, accommodate the shape of the wearer, and do it for long periods continuously.

High-Efficiency Solar Cells for IoT Devices

As per expert estimates, by 2025, the worldwide number of IoT, or the Internet of Things, could rise to 75 billion. However, most IoT devices have sensors that run on batteries. Replacing these batteries can be a problem, especially for long-term monitoring.

Researchers at the Massachusetts Institute of Technology have now produced photovoltaic-powered sensors. These sensors can transmit data potentially for several years, before needing a replacement. The researchers achieved this by mounting thin-film perovskite cells as energy harvesters on low-cost RFID or radio-frequency identification tags. Perovskite cells are notoriously inexpensive, highly flexible, and relatively easy to fabricate.

According to the researchers, the future will have billions of sensors all around. Rather than power the sensors with batteries, the photovoltaic-powered sensors could use ambient light. It would be possible to deploy them and then forget them for months at a time or even years.

In a pair of papers the researchers have published, they have described the process of using sensors to monitor indoor and outdoor temperatures continuously over many days. No batteries were necessary for the sensors to transmit a continuous stream of data over a distance greater than five times that traditional RFID tags could. The significance of a long data transmission range means the user can employ one reader for collecting data simultaneously from multiple sensors.

Depending on the presence of moisture and heat in the environment, the sensors can remain under a cover or exposed for months or years before they degrade enough requiring a replacement. This can be valuable for applications requiring long-term sensing indoors as well as outdoors.

For creating self-powered sensors, many other researchers have tried solar cells for IoT devices. However, in most cases, these were the traditional solar cells and not the perovskite type. Although traditional solar cells can be long-lasting, efficient, and powerful under certain conditions, they are rather not suitable for universal IoT sensors.

The reason is, traditional solar cells are expensive and bulky. Moreover, they are inflexible and non-transparent—suitable and useful for monitoring the temperature on windows and car windshields. Most designs of traditional solar cells allow them to effectively harvest energy from bright sunlight, but not from low levels of indoor light.

On the other hand, it is possible to print perovskite cells using easy roll-to-roll manufacturing techniques costing only a few cents each. They can be made into thin, flexible, and transparent sheets. Furthermore, they can be tuned to harvest energy from outdoor or indoors lighting.

Combining a low-cost RFID tag with a low-cost solar power source makes them battery-free stickers. The combination allows for monitoring billions of products all over the world. Adding three to five cents more, it is possible to add tiny antennas working at ultra-high frequencies to the stickers.

Using a communication technique known as backscatter, RFID tags can transmit data. They reflect the modulated wireless signals from the tag and send it back to their reader. The reader is a wireless device, very similar to a Wi-Fi router, and it pings the tag. In turn, the tag powers up and using backscattering, sends a unique signal with information about the product on which it is stuck.

Energy from Vibrations for IoT Devices

Producing energy from vibrations is nothing new, and the world is always hungry for more clean energy. Engineers now have a new material that can convert simple mechanical vibrations all around it, to electricity. The electricity is enough to power most sensors on the Internet of Things ranging from spacecraft to pacemakers.

Engineers at the University of Toronto and the University of Waterloo have produced the material after decades of work. Their research has generated a novel compact electricity-generating system that they claim is reliable, low-cost, and green.

According to the researchers, their achievement will have a significant impact on social and economic levels, as it will reduce the reliance on non-renewable energy sources. They claim the world needs these energy-harvesting materials critically at this moment in time.

Energy harvesting technology produces small amounts of energy from external effects such as heat, light, and vibrations. For instance, an energy-harvesting device worn on the body could generate energy from body movements, such as from the legs or arm movements while walking. Most such devices produce enough energy to power personal health monitoring systems.

Based on the piezoelectric effect, the new material that the researchers have developed generates an electric current when there is pressure on it. Mechanical vibrations are one example of the type of pressure on the appropriate substance.

The piezoelectric effect is known and in use since 1880, and people have been using many piezoelectric materials like Rochelle salts and quartz. The technology has been in use for producing sonars, ultrasonic imaging, and microwave devices.

However, until now, most traditional piezoelectric materials in use in commercial devices have had a low finite capability for generating electricity. Moreover, most of these materials use Lead, which is detrimental to the environment and to human health as well.

The researchers solved both the above problems in one go. They grew a single large crystal of a molecular metal. This was a halide compound known as edabco copper chloride. For this, they used the Jahn-Teller effect, which is a well-understood concept in Chemistry, and offers a spontaneous geometric distortion in the crystal field.

The researchers proceeded to fabricate nanogenerators with the highly piezoelectric material they had produced. The nanogenerators had a significant power density and could harvest small mechanical vibrations in many dynamic circumstances involving those from automobile vehicles and even human motion. The nanogenerators neither used Lead nor needed non-renewable energy sources.

Each nanogenerator is just a shade smaller than an inch square, or 2.5 x 2.5 cm, and the thickness of a business card. It is possible to use them in various situations. They have a significant potential for powering sensors in vast arrays of electronic devices, such as those used by IoT or the Internet of Things, of which the world uses billions, and requires substantially more.

According to the researchers, the new material could have far-reaching consequences. For instance, the vibrations from an aircraft would be enough to power its systems for monitoring its various sensors. On the other side, vibrations from a person’s heartbeat could power their pacemaker, which can run without a battery.

Edge Computing for Smart Homes

Designing devices for smart homes can be a huge challenge. There are numerous limitations to be overcome, but the sensible use of sensors can help smooth the way. Devices for smart homes can relate to lighting, kitchen appliances, security, heating/cooling, and entertainment. With the advancement in technology for smart homes, engineers need to be more intuitive and develop more capabilities for making products more intelligent. Among the expectations from homeowners are faster response, higher performance, higher levels of accuracy, and easier integration of multiple devices.

Today, there are widely varying intelligent devices in modern intelligent home technology. Most often, these produce massive amounts of data that must be processed quickly. Although there are limitations to improving the technology for smart homes, contextual data can address them by using a combination of sensors, with the device processing them on the field rather than doing it in a cloud.

Just like in any technology, the fundamental systems and components of smart home technology are also constantly improving. Engineers must continuously develop better solutions as soon as they recognize the limitations. Among the several limitations, three major ones that plague smart home technology are accuracy, latency, and compatibility.

Accuracy is an extremely important factor in smart home technology. Everything affects accuracy, starting from the sensors that are necessary to collect data to the artificial intelligence tools that process the data. This is leading engineers to collect data using innovative new approaches, including using algorithms to combine multiple sensors for processing the data so that they can achieve a higher level of accuracy.

For instance, a smart home security system may involve radar, computer vision, and sound detection to accurately predict the presence of a person. Engineers are also using AI tools and algorithms for finding the most efficient methods of processing data. However, this leads us to the next limitation—latency.

Latency negatively impacts any type of smart home technology. Home security, for instance, needs collecting data from multiple sensors, and analyzing them as fast as possible. The impact on latency increases as there is an increase in the data gathered, transmitted, and processed.

With end users having multiple smart systems working concurrently, compatibility challenges are bound to crop up, impacting overall performance and functionality. This is one reason for engineers to move their focus from systems that depend on platforms, manufacturers, and devices. Rather, they are moving more of the functionality and processing to the devices themselves. This is where edge computing is helping them—addressing all three challenges at a time.

In smart home technology, edge computing transfers most of the processing and analysis from the cloud to the device itself. In simpler terms, data processing takes place as close to the sensor as possible.

For instance, home security cameras are notorious for reporting false positives, eventually causing the owners to ignore accurate alerts. One way of improving the accuracy is by improving the quality of the lens and image sensors. The other is by using edge computing to differentiate between the movement of animals and leaves being moved by winds.

The Function of Ferrites in Electronics

Engineers often use ferrite components in electronic circuits. These ferrite components are nonconductive, ceramic compound materials made with numerous combinations of iron oxides. Electronic components typically use them because of their high electrical resistivity and low eddy current losses. Ferrites can have various properties depending on their condition of synthesis, sintering temperature, composition, and grain size.

Manufacturers classify ferrites based on their crystal structure and magnetic properties. In general, they are of two types—soft and hard. Soft ferrites, made from magnesium, manganese, nickel, cobalt, and zinc, have low coercivity, such that their magnetism changes easily, and they act as conductors of magnetic fields. On the other hand, hard ferrites make very good permanent magnets, owing to their high coercivity.

It is also possible to classify ferrites based on their crystal structure. Typically, there are four groups— spinel, garnet, ortho, and hexagonal. Manufacturers distinguish them based on the molar ratio of ferric oxide to other oxide compounds present in the ferrite ceramic.

Crystallizing spinel ferrite results in a cubic structure with oxygen anions in a closely packed arrangement. Here, a unit cell comprises 32 oxygen ions. The anions form an FCC or face-centered cubic array.

Ferrites typically exhibit a permanent type of magnetism that physicists refer to as ferrimagnetism. This is a phenomenon that aligns the magnetic moments of atoms in both antiparallel and parallel directions. This alignment partially cancels the magnetic field, making the overall magnetic field of a ferrite material weaker than that of ferromagnetic materials.

Various types of ferrites are available. In electronic circuits, engineers typically use them as beads. For a ferrite bead, the resistivity is the strongest in a thin frequency band. This feature makes ferrite beads very useful as frequency-dependant resistors. Above the frequency band, the impedance of the bead begins to appear capacitative.

Other types of ferrites structures are also available for use in electronics. For instance, there are flat ferrites, typically rectangular or disc-shaped. Engineers use them in applications where they need a flat shape, such as power inductors, planar transformers, filters, and power inductors. Flat ferrites are very useful for suppressing radio frequency interference and electromagnetic emissions.

Ferrite rings and sleeves are also available. These are cylindrical-shaped components, suitable for placing around a wire or cable. It acts like a filter that can block high-frequency noise, allowing only low-frequency signals to pass through the wire or cable. Manufacturers choose the inner diameter of the ferrite to closely match the outer diameter of the cable, as this maximizes the benefits of interference suppression. Ferrite rings and sleeves are very useful in applications like data communications, consumer electronics, and power supplies to improve signal integrity and reduce interference effects on circuit performance.

Multi-hole ferrite beads are cylindrical cores with typically 6 through-holes running along the axis of the cylinder. When a trace or wire in a circuit is wound through its holes, the multi-hole ferrite bead behaves as a low-pass filter. It blocks unwanted high-frequency interference signals and allows only low-frequency signals to pass through the wire.

3D Printed RF Components

Most RF system designers view air simply as a medium for electromagnetic energy propagation from the source to the receiver. This is usually the case, allowing them to focus the bulk of their design effort on interconnections and integrated circuits that define the physical system.

However, that is only a simplistic view, as other properties of air are also important. For instance, air can keep electronics cool with convection, and it has dielectric properties that some RF components find critical.

Heinrich Hertz first demonstrated wireless signals in 1888. He energized a spark gap of 1 millimeter using high voltage, creating a wideband pulse. A dipole antenna transmitted this pulse. The antenna had two collinear metal rods with capacitive metal plates. At standard atmospheric conditions, air has a dielectric strength of about 30-70 volts/mil or 3-7 kV/mm. Discharged through air across the gap, the high voltage spark caused brief standing waves of oscillating current in the antenna, which then radiated this energy as a brief pulse of radio waves.

With the growth and maturing of wireless, RF tuners often had variable capacitors. These consisted of multiple parallel plates with air gaps that decided the capacitance value of the tuning assembly. By rotating a shaft, it was possible to adjust the position of the moving plates with reference to the static ones, thereby changing the capacitance between them from near zero to several hundred picofarads.

Vacuum has the ideal unit dielectric constant, while air is very close, with a value of 1.00058986. In comparison, the dielectric constant of PTFE is 2.0, and for FR4 it is about 4.4.

Another important property of vacuum, is its dielectric loss, dissipation factor, or loss tangent is zero, and so it is for air as well. Moreover, air characteristics are stable well into the terahertz frequency range, but it is not so for other dielectrics.

However, both vacuum and air have a common weakness. Neither has any structural strength. Therefore, they require a supporting form to hold them. Engineers find this a challenge as there must be an adequate amount of air within the structural medium of the dielectric.

The solution to this problem lies in using AM or additive manufacturing, also known as 3D printing, along with foam, and a family of photopolymer materials. Roger’s Corp typically supplies specialty RF materials, such as the Radix family of 3D printable, high-resolution materials. Radix is a photo-curable, highly viscous resin. It is a high filler concentration that offers good mechanical and electrical properties even at high frequencies.

3DFortify, of Boston, makes a particular type of Flux Core 3D printer. This is the only printer in the market that can effectively print using the Radix resin. The two companies are now partnered to produce 3D-printed RF components.

The printer layers the material with a thickness of less than 100 µm and cures it with a UV digital light processing projector in one flash for every layer. They provide both metalized and non-metalized versions. With the 3D-printing approach, the manufacturer can vary the structural strength of the material as necessary. They can give thick and strong structures at places subject to physical pressure or connections. 

DAWSense Turns Any Surface into an Input Device

Although we are used to traditional interfaces like touchscreens and keyboards, interfacing with computers has traversed a long distance over the years. Now, it is possible to turn any surface into an input device. DAWSense can do this by utilizing machine learning and taking advantage of surface acoustic wave technology. With different situations requiring varying methods of input, researchers are now exploring newer methods of human-computer interfacing. One of them is to embed the interface within everyday objects, thereby enhancing user experiences.

Human-machine interfaces may take many forms. For instance, the industry often uses microphones or cameras to control devices using methods like speech or gesture recognition. Although such systems may be of immense help in certain applications, they may not be practical for others. In a camera-based system, it is easy to obscure the arrangement by introducing objects in front of the camera. Similarly, microphone-based systems involving speech recognition may not function properly in noisy environments.

As an alternative, researchers were experimenting with transforming any arbitrary surface to act as an input device. For instance, for controlling a smart home, they have experimented with the arm of a couch acting as a TV remote, or an interactive wall. They have tried many methods for building such functionality so far, with accelerometers standing out as one of the most promising solutions, as they can sense touch gestures on various surfaces without any modifications on them.

However, the sampling bandwidth of accelerometers incorporated into a surface to act as a touch-sensing device is not enough to capture more than a few relatively coarse gestures. Now, a collaboration between researchers at the Meta Reality Labs and the University of Michigan has demonstrated another method that offers the necessary bandwidth for creating user interfaces that are more advanced.

The new method relies on SAWs or surface acoustic waves rather than mechanical vibrations for sensing touch inputs. The team has also fashioned a VPU or voice pick-up unit for detecting subtle touch gestures. They have designed the VPU to conduct the surface waves into a hermetically sealed chamber that contains the actual sensor. This practically removes any interference from background noise. As the team has fabricated each VPU using the MEMS process, the sensor has the necessary high bandwidth that is typically associated with a MEMS microphone.

Although the MEMS sensor was a high-performance one, the researchers still needed a method for converting the SAWs into swipes, taps, and other gestures. A hard-coded logic would fail to convert them satisfactorily, so the team had to design a machine-learning model with an algorithm to learn from the data.

VPUs typically collect a huge amount of data, and processing this data on an edge computing device in real-time would be a challenge. The researchers dealt with this problem by calculating Mel-Frequency Cepstral Coefficients, which helped in understanding the most informative features of the data. With this analysis, the researchers could reduce the number of features they needed to consider from 24,000 to just 128. They then fed the features into a Random Forest classifier for determining the exact representation of the surface waves.

FireBeetle Drives Artificial Internet of Things

The next generation of the FireBeetle 2 development board is now available. Targeting the IoT, especially the Artificial Intelligence of Things, it has an onboard camera. According to DFRobot, the creator, the FireBeetle boasts Bluetooth and Wi-Fi connectivity, and an Espressif ESP32-S3 module.

Built around the ESP32-S3-WROOM-1-N16R8 module, the main controller of the FireBeetle provides high performance. It operates with 16MB of flash RAM, along with 8MB of pseudo-static RAM or PSRAM that allows it to store more data. The ESP32-S3 chip provides acceleration for computing neural networks and processing signals for high workloads. This makes the FireBeetle ideal for many applications like image recognition, speech recognition, and many more.

DFRobot has designed the heart of the FireBeetle, the ESP32-S3, for edge AI and low-power tinyML work. With two CPU cores, the Tensilica Xtensa LX7, both operating at 240 MHz, the ESP32-S3 also offers vector processing extensions. The design specifically targets accelerated machine learning, including workloads of artificial intelligence. In addition to the 8MB PSRAM and the 16MB Flash memory, the board also has 384kB of flash and 512kB of on-chip SRAM.

The FireBeetle development board, along with its BLE or Bluetooth 5 Low Energy and Wi-Fi connectivity, also includes an onboard camera interface driven by a dedicated power supply circuit. The camera has a 2-megapixel sensor with a 68-degree FOV or Field of Vision. There is a GDI connector, which is useful for adding a TFT display.

DFRobot offers two variants of the FireBeetle development board. One of them is the standard version, namely the FireBeetle 2 ESP32-S3, containing a PCB antenna for wireless connectivity. The second variation is the FireBeetle 2 ESP32-S3-U, and it offers a connector for rigging up an external antenna. It is possible to program both boards from Arduino IDE, ESP-IDF, and MicroPython.

It is possible to order both development boards from the DFRobot website store, The second variant is the costlier of the two, and both come with volume discounts. Although both variants come with the board and camera, the pin headers are bundled loosely but not soldered. DFRobot has published a simple project for the FireBeetle—a camera-based monitor to oversee the growth of plants.

It is possible to use the FireBeetle development board to build a DIY plant growth recorder. It allows monitoring the entire growth process of the plant, starting from seeding right up to maturity, while tracking the environmental conditions throughout. This makes it possible to identify any changes easily that could affect the health and growth of the plant, along with any fluctuations in temperature, light levels, and humidity. This information helps to organize and optimize the growing conditions of the plant, thereby ensuring that the plants get everything they need for proper growth.

The project has a screen for displaying the various parameters it is monitoring. The camera periodically captures images of the plant as it grows, storing them in the board’s memory. The board transmits real-time images and environmental data over Wi-Fi or Bluetooth for regular viewing.

Preserving IoT Battery Life

At MIT, researchers have built a wake-up receiver for IoT devices. The receiver uses terahertz waves to communicate, making the chip more than ten times smaller than contemporary devices. The receiver also includes authentication that helps protect it from certain types of attacks. The low power consumption of the chip means it can help preserve battery life in robots or tiny sensors.

The current trend is towards developing ever-smaller devices for IoT or the Internet of Things. For instance, sensors can be smaller than a fingertip, capable of making any object trackable. Most of these tiny sensors, however, have even tinier batteries that are nearly impossible to replace. Therefore, engineers need to incorporate a wake-up device in these sensors. It keeps the device in a low-power sleep mode when not operating, thereby preserving battery life. The new device from MIT is capable of protecting the device from certain attacks that could drain its battery rather quickly.

The present generation of wake-up receivers is typical of the centimeter scale. This is because their antennas need to be proportional to the length of the radio waves they use for communicating. On the other hand, the MIT team utilized the terahertz wave for the receiver. As these waves are about one-tenth the length of regular radio waves, they could design the chip to be barely greater than a square millimeter.

It is possible to incorporate the wake-up receiver into microbots for monitoring environmental changes in locations that are either hazardous or too small for other robots to reach. As the device operates on terahertz frequencies, it is possible to use them in emerging applications like radio networks that operate as field-deployable swarms for collecting localized data.

Using terahertz frequencies, the researchers could make antennas the size of a few hundred micrometers on either side. The implication of such small-size antennas is that it is possible to integrate them on the chip, thereby creating a totally integrated solution. Ultimately, the researchers could build a wake-up receiver tiny enough to attach to tiny radios or sensors.

On the electromagnetic spectrum, terahertz waves exist between infrared light and microwaves. At very high frequencies, they travel much quicker than radio waves can. Terahertz waves, also known as pencil beams, travel in a rather direct path as compared to other signals, making them more secure.

However, terahertz receivers often multiply their signal by another signal so that they can alter their frequency. This process is termed frequency mixing or modulation, and it consumes a huge amount of power. The researchers at MIT used a pair of tiny transistors as antennas for detecting terahertz waves. This method of detecting consumes very little power, as it does not involve frequency mixing.

Even when they placed both antennas on the chip, the MIT wake-up chip was only 1.54 square millimeters and used only 3 microwatts to operate. The presence of two antennas maximizes its performance and makes it more sensitive to receiving signals. Once it detects the terahertz signal, it converts the analog signal into digital data for processing. The received signal contains a token, which, if it matches the wake-up receiver’s token, will activate the device.

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.