“Monitors in industrial, IoT, home medical wearables, fitness and health are experiencing explosive growth as systems become increasingly data-centric. These data-centric systems are increasingly demanding more functionality and lower power consumption. This trend is driven by intelligent systems that actively monitor a person or environment and respond predictively, including alerts, actions, or recommended actions. The response depends on the data provided, which is why these systems need to collect large amounts of high-precision data from a single sensor or wireless sensor network.
Monitors in industrial, IoT, home medical wearables, fitness and health are experiencing explosive growth as systems become increasingly data-centric. These data-centric systems are increasingly demanding more functionality and lower power consumption. This trend is driven by intelligent systems that actively monitor a person or environment and respond predictively, including alerts, actions, or recommended actions. The response depends on the data provided, which is why these systems need to collect large amounts of high-precision data from a single sensor or wireless sensor network.
The challenge for sensor application design engineers is to require a sensor module that minimizes board area while maintaining high accuracy and extending battery life. To address this challenge, there are two responses: one is to maximize the energy efficiency ratio of components and system operations, and the other is to invest in the development of new low-power architectures. The first approach, which focuses on developing systems that run on batteries for longer and provide greater responsiveness and precision, promises to help designers achieve their goals in the short term.
Maximize power efficiency
Figure 1. The design used by current AI systems is shown in the sensor block diagram above.
Figure 1 above shows a typical block diagram of a sensor application. The four basic blocks of the solution are system power, sensors, sensor signal amplification and signal processing. Choosing the right device is critical to maximizing the battery life of the sensor module. Below we take a closer look at each module to see what can be done to improve power efficiency and provide more accurate measurements.
The first consideration is the sensor. There are two main types of sensors used in sensor modules today: single-ended and differential. Single-ended sensors include electrochemical sensors for blood glucose detection, gas sensors, and wearable medical sensors. Differential sensors typically use instrumentation amplifiers, and applications include industrial pressure or force sensors, industrial temperature sensors, air-inline and occlusion sensors in medical applications, and more. These are common in medical insulin pumps and in-line air detectors.
A more common type of sensor is an electrochemical sensor. These are low-power sensors, including blood sugar sensors, which are used by millions of people with diabetes to control their blood sugar levels. Other applications include gas sensors such as carbon dioxide (CO2) sensors, water quality (conductivity, pH, etc.) sensors, alcohol sensors for oil degradation, and sensors for detecting explosives.
Most applications for electrochemical sensors are portable and battery powered applications. While home CO2 sensors typically last five to seven years, a new battery may need to be replaced approximately every six months to a year. To extend battery life, manufacturers use the latest low-power devices that draw very little current from the battery.
Next we take a closer look at a specific type of electrochemical sensor, the ethanol sensor, and understand how it works.
How the ethanol sensor works
The ethanol sensor used in Figure 1 is an amperometric gas sensor that produces a current proportional to the volume fraction of the gas. It is a three-electrode device where ethanol is measured at the working (or detection) electrode (WE). The counter electrode (CE) completes the circuit, while the reference electrode (RE) provides a stable electrochemical potential in the electrolyte, which is not exposed to ethanol. For the SPEC sensor, a +600mV bias voltage was applied to RE.
Many electrochemical sensors require a fixed bias to function properly, which places an additional burden on battery life. Now we consider the power requirements of the system.
The power budget of the system and its battery capacity ultimately determine the operating life of the sensor. A typical goal for a small form factor battery powered solution is to use a single 1.5V battery. Using a single-cell battery reduces the capacity, which affects the operational life of the sensor. So, what can be done to optimize the operating life of a single cell?
When fully charged, i.e. at the beginning of its life, a single cell is 1.5V. This voltage gradually decreases over time and is 0.9V at the end of life. To maximize single-cell battery life, the application must run between 0.9V and 1.5V for maximum application operating time. Since other system devices operate at 1.8V, a DC-DC boost converter must be selected that maximizes active and standby current efficiency and operates from 0.9V to 1.5V.
Having a high efficiency of 95% is not the only consideration for efficient power conversion. The boost regulator must also be able to operate efficiently over a wide current range, thereby reducing quiescent current (IQ) and heat dissipation during operation. The application spends most of its time in standby mode, so the boost converter must have high efficiency in light-load standby to prolong battery life. The shutdown feature reduces current consumption to the nA range by shutting down portions of the circuit, which also significantly reduces power consumption.
Signal Chain Solutions
The output signal produced by the sensor is usually very weak, only a few uV, and the analog-to-digital converter requires a V-level signal. Therefore, choosing a low-power, high-precision amplifier is the second most important design consideration.
Low power amplifiers have two important aspects – current consumption and operating voltage, since many sensors require bias current to maintain accuracy. This requires the sensor portion of the application to be turned on to maintain accurate readings. In addition, the low operating voltage of 0.9V to 1.5V supports single-cell battery operation, eliminating the need for a boost converter.
In general, the downside of choosing a low-power amplifier is lower accuracy. However, there are low power amplifiers that maintain a high level of accuracy even at low operating currents and voltages. Some of the characteristics of precision amplifiers include sub-microvolt (µV) input offset voltage, nV/°C voltage drift, and pA input bias current.
A low-power microcontroller combined with an integrated ADC provides a low-power sensor solution that maximizes battery life while keeping the application small.
Measurement of Ethanol Sensor Solutions
In addition to device-level improvements, system architectures can be optimized to achieve lower power consumption with the same level of precision measurements. To demonstrate this, we will provide two experimental measurements of an ethanol sensor solution using similar devices, and one theoretical measurement of a future sensor solution, the latter showing the benefit of saving electricity.
This experiment used the devices listed below, which have the same duty cycle for ethanol electrochemical sensor measurements.
• SPEC Electrochemical Ethanol Sensor
• MAX40108 1V precision op amp/1.8V op amp
• MAX17220 0.4-5.5V nanoPower synchronous boost converter with True Shutdown™
• MAX6018A 1.8V Precision, Low Dropout Reference
• MAX32660 1.8V ultra-low-power Arm® Cortex®-M4 processor
• Single 1.5V AA battery
Traditional 1.8V system
Figure 2. The above figure shows a traditional 1.8V sensor system solution.
The 1.8V system solution operates from a single cell battery and utilizes a high-efficiency boost converter to provide 1.8V system power for the ethanol sensor, op amp, and microprocessor with ADC. The 0.1% active duty cycle is controlled by the microcontroller, which is measured after the microcontroller wakes up and then goes back to sleep mode.
The sensor in standby mode utilizes a boost converter to maintain power to the sensor, op amp, and microcontroller in sleep mode. In standby, the system consumes 150.8µA of current. During the active state, the microcontroller wakes up and takes sensor measurements. In active state, the system consumes 14mA for a short time. The active state is only 0.1% of the time, and the combined active and standby modes are calculated to have an average current of 164µA, which is typical for real sensor applications.
1V Amplifier System
Figure 3. Shown above is a next-generation 1V amplifier sensor solution.
In the 1V amplifier solution, both the SPEC ethanol sensor and the MAX40108 1V op amp are connected directly to the battery. This requires an amplifier that can operate down to 0.9V, maintain a level of precision, and maximize single-cell battery life.
The rest of the circuit is similar to the boost regulator that powers the microcontroller and supports the 1.8V circuit. In this configuration, the current is significantly reduced to 81.9µA, a reduction of 45%; the average current is reduced to 95.7µA, a reduction of 41.79%. As a result, the battery life of a system using the MAX40108 1V op amp is nearly double that of a conventional system.
1V Signal Chain Systems of the Future
Figure 4. The block diagram above shows a future 1V sensor system solution.
In this future 1V signal chain solution, amplifiers, ADCs, and microcontrollers all operate down to 0.9V while maintaining high levels of precision. This allows the entire signal chain solution to be powered from a single cell, eliminating the need for a boost converter and maximizing battery life for the sensor solution.
The demand for smarter AI systems is growing, and so is the need for sensors with additional functionality, higher accuracy and longer life. Sensors must provide a small form factor solution that can be worn by a person or networked to determine the health of a person, production floor, building or city, enabling systems to respond proactively rather than reactively. Going a step further, for those who benefit from next-generation systems, proactive response can improve health, reduce costs, increase productivity, and enhance safety.
Innovation is happening at many different levels in sensor networks that enable AI systems. IC manufacturers, in particular, are developing lower-power sensor building blocks to help today’s engineers create smarter, more efficient systems for tomorrow.
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