Editor's Note: As the "nerve endings of the information age," sensors have penetrated every critical field of the socio-economic landscape. Since October 2025, China Electronics News has invited Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society and Executive Vice Chairman of the China Sensor and IoT (Internet of Things) Industry Alliance, to launch a column titled "Guo Yuansheng Explains Sensors in Detail." The column focuses on eight major fields and scenarios: electric power, major equipment, intelligent manufacturing, smart agriculture, smart healthcare and big health, smart home appliances and consumer electronics, urban security, and low-altitude economy. Articles such as "Sensors on the Power Generation Side: The Cornerstone of Stable Operation in New Power Systems" and "Energy Storage Sensors Clarify Three Core Development Directions" have been published successively, receiving widespread attention and high praise from readers. This issue publishes the third article in the consumer electronics field, focusing on smartphone sensors, and elaborates on their application prospects, industrial status, and future suggestions to build industry consensus and promote industrial development.
Smartphones are essential terminals in modern life, and their intelligent upgrades are inseparable from the support of smart sensors. From environmental perception and motion monitoring to biometric recognition, sensors serve as the "nerve endings" and "interaction windows" of smartphones, supporting the realization and innovation of core functions.
For this reason, smartphones have become the platform with the most concentrated application and the fastest iteration of sensor technology: the demand for slim and light smartphones forces breakthroughs in sensor technology, while sensor innovations rapidly penetrate mass scenarios such as mobile payments and smart navigation through smartphones.
I. Types, Parameter Indicators, Performance, and Technical Defects of Smartphone Smart Sensors
As mobile smart terminals, smartphones integrate various sensor technologies and products internally, forming diverse types and refined parameter configurations to support precise perception of the external environment and user behavior. Different types of sensors have their own focuses in parameter indicators and performance, while also having certain defects due to technical limitations or application scenario differences.
Acoustic Sensors
Smartphone acoustic perception mainly includes microphones and speakers (earpieces and external loudspeakers), and their performance directly determines the user experience in scenarios such as voice calls and audio playback.
Smartphones typically use miniature capacitive microphones, which generate electrical signals by sensing sound vibrations through a diaphragm, featuring small size and high sensitivity. Some high-end models are equipped with multiple microphones in an array layout to achieve directional sound pickup and noise reduction. Speakers are divided into dynamic and piezoelectric types. Dynamic speakers, with mature structure and controllable cost, are the mainstream choice, while piezoelectric speakers, though smaller, have relatively limited sound quality and are mostly used in the earpiece design of slim models. The core parameters of microphones are sensitivity, frequency response, and signal-to-noise ratio (SNR). Sensitivity is usually between -30dB and -40dB; the closer the value is to 0dB, the stronger the ability to capture weak sounds. The frequency response range generally covers 20Hz to 20kHz; the wider the range, the better it can restore sound details. For example, the frequency response of high-end models can extend above 20kHz, improving the accuracy of voice recognition. SNR reflects the anti-interference ability of the microphone, generally requiring 60dB or above to avoid environmental noise mixing into the collected signal. The key parameters of speakers include power, frequency response, and distortion. The power of smartphone external speakers is mostly between 1W and 2W; excessive power can easily cause body vibration and needs to be optimized with sound cavity design. The frequency response focuses on mid-to-high frequency performance to meet the playback needs of human voice and music, while the low-frequency part is often difficult to fully restore due to the limited volume of the smartphone sound cavity. Distortion needs to be controlled below 1% to avoid broken sound during high-volume playback.
In terms of performance, microphones can accurately capture voice signals within 3 meters in a quiet environment. Combined with noise reduction algorithms, they can effectively filter background noise in noisy environments (such as subways and shopping malls) to ensure call clarity. Speakers can achieve clear human voice playback at normal volumes. Some models supporting stereo sound effects can provide a certain sense of space through the coordination of left and right channels. However, in strong noise environments (such as construction sites and concert venues), their sensitivity is easily interfered with, leading to distorted voice collection. Even with noise reduction enabled, it may not be completely filtered if the noise frequency is close to the voice. When speakers work at high power for a long time, the diaphragm will fatigue due to continuous vibration, leading to degraded sound quality, such as muddy bass and harsh treble. Meanwhile, the slim design of smartphones compresses the sound cavity volume, making the low-frequency response of speakers always limited and unable to achieve the sound quality of professional audio equipment. In addition, the waterproof and dustproof performance of acoustic sensors is weak. Once water or dust enters the body, it may cause blurred microphone pickup, reduced speaker volume, or even damage, which is a common problem faced by slim models.
Optical Sensors
The core functions of smartphones such as image capture, ambient light adaptation, and facial recognition mainly include camera modules, ambient light sensors, and infrared sensors. Their technological iteration directly promotes the upgrade of smartphone imaging capabilities and interactive experiences.
Camera modules cover various lenses such as main camera, ultra-wide-angle, telephoto, and macro. Different lenses have different tasks: the main camera is responsible for the image quality output of daily photography, mostly using large-size sensors to increase light intake; the ultra-wide-angle lens has a wider field of view, suitable for landscapes and group photos; the telephoto lens achieves long-distance shooting through optical zoom, and some models use a periscope structure to increase the zoom ratio; the macro lens focuses on capturing details of close objects. Ambient light sensors are mostly photoresistors or photodiodes, used to perceive external light intensity. Infrared sensors (integrating transmitting and receiving ends), combined with infrared cameras and facial recognition algorithms, achieve liveness detection and night unlocking functions.
The core parameters of camera modules include sensor size, pixel count, aperture size, optical zoom ratio, and shutter speed. Sensor size is usually expressed in inches, such as 1/1.3 inch or 1/1.56 inch. The larger the size, the more sufficient the light intake and the better the image quality in low light. Pixel counts range from tens of millions to over 100 million, such as 48 megapixels or 100 megapixels. High pixels help improve photo details but require excellent image processing algorithms; otherwise, noise is prone to appear. Aperture size is expressed in F-values, such as F1.5 or F2.0. The smaller the F-value, the larger the aperture and the more light intake, suitable for low-light photography. Optical zoom ratios range from 3x to over 10x. Periscope telephoto lenses can achieve 10x optical zoom, avoiding image quality loss caused by digital zoom. The shutter speed range is generally between 1/4000s and 30s. High-speed shutters can capture moving objects, while slow shutters are suitable for long-exposure scenes (such as starry sky photography). The parameters of ambient light sensors are mainly sensitivity and response range. Sensitivity needs to perceive light changes from 10lux to 10000lux. The wider the response range, the more precise the screen brightness adjustment. The parameters of infrared sensors include transmitting power and receiving sensitivity. The transmitting power needs to ensure that the infrared signal covers the face area, and the receiving sensitivity needs to accurately identify the reflected infrared signal to avoid accidental unlocking.
In imaging and interaction scenarios, camera modules can take photos with accurate color reproduction and rich details in well-lit daytime conditions. The dynamic range of the main camera can balance the details of highlights and shadows. Although the ultra-wide-angle lens has a certain edge distortion, it can be effectively improved through algorithm correction. In low-light environments, combined with a large aperture, it can significantly improve screen brightness and reduce noise. Some models support night mode, further optimizing image quality through multi-frame synthesis. Ambient light sensors can perceive light changes in real time to achieve automatic adjustment of screen brightness. The response time is generally between 0.5s and 1s, ensuring that the screen brightness can quickly adapt when users move in and out of indoors and outdoors, avoiding glare or excessive darkness. Infrared sensors, combined with facial recognition algorithms, can achieve fast unlocking within 0.3 seconds. Even in a lightless environment at night, recognition can be completed through infrared fill light, and it has high security, effectively resisting attacks from forged means such as photos and videos.
However, optical sensors also have many defects. In terms of camera modules, the higher the optical zoom ratio of the telephoto lens, the more complex the structure, leading to an increase in body thickness, which contradicts the trend of slim and light smartphones. Some models have to sacrifice the optical quality of the telephoto lens to control thickness and use digital zoom assistance, resulting in degraded image quality for long-distance shooting. The focusing distance of the macro lens is short, generally between 2cm and 5cm. Beyond this range, focusing blur is prone to occur, and the image quality is far inferior to professional macro cameras. In strong direct light (such as noon sunlight) or weak flickering light (such as candlelight) environments, the response speed of the ambient light sensor will slow down, resulting in untimely screen brightness adjustment. Meanwhile, the installation position of some ambient light sensors is unreasonable (such as the top bezel of the screen), which can be easily blocked by fingers or phone cases, affecting perception accuracy. The infrared signal of infrared sensors is easily interfered with by strong light. Under direct noon sunlight in summer, facial recognition unlocking may fail. In addition, the detection range of infrared sensors is limited. When the user's face is too far from the screen (over 30cm) or the angle is too large (tilted over 30 degrees), the recognition success rate will significantly decrease.
Mechanical Sensors
Used to perceive the motion state, force conditions, and user touch operations of smartphones, they are the foundation for realizing functions such as screen rotation, step counting, and touch interaction. Common types include acceleration sensors, gyroscopes, pressure sensors, and touch sensors.
Acceleration sensors are used to detect the linear acceleration of smartphones in three-dimensional space, which can determine the direction and speed of the phone's motion. Gyroscopes are used to detect the angular velocity of the phone, assisting acceleration sensors to improve the accuracy of motion perception. The two work together to achieve precise posture recognition. Pressure sensors are divided into barometric pressure sensors and pressure touch sensors. Barometric pressure sensors measure atmospheric pressure to assist positioning functions in improving the accuracy of altitude detection. Pressure touch sensors are integrated under the screen to perceive the force of the user pressing the screen, realizing pressure-sensitive operations (such as long-press shortcut functions and drawing pressure adjustment). Touch sensors are mostly capacitive, realizing touch recognition by detecting the capacitance change between the human finger and the screen. Some high-end models use ultrasonic touch technology to improve touch accuracy with wet hands or when wearing gloves.
The core parameters of acceleration sensors are measurement range, sensitivity, and response frequency. The measurement range is generally ±2g to ±16g (g is the acceleration due to gravity). The wider the measurement range, the better it can adapt to the detection of intense motion scenes (such as running and jumping). Sensitivity needs to reach 0.01g to ensure the perception of micro acceleration. The response frequency is generally between 10Hz and 100Hz. The higher the response frequency, the more timely the capture of fast motion. Gyroscope parameters include measurement range and drift rate. The measurement range is usually ±2000dps (degrees per second), which can detect the fast rotation of the phone. The drift rate needs to be controlled within 0.1dps to avoid posture detection errors after long-term use. Among pressure sensors, the measurement range of barometric pressure sensors is generally 300hPa to 1100hPa, and the accuracy needs to reach ±1hPa to ensure the altitude detection error is within 10 meters. The measurement range of touch pressure sensors is 10g to 400g, and the sensitivity needs to reach 1g to distinguish different pressing forces. The parameters of touch sensors include touch points, response speed, and resolution. The number of touch points generally supports 10-point or more multi-touch. The response speed needs to reach within 10ms to avoid touch delay. The resolution is consistent with the screen resolution to ensure accurate identification of touch positions.
Acceleration sensors and gyroscopes work together to achieve precise screen rotation, with a response time within 0.1 seconds, and can accurately count steps with an error rate controlled at around 5%. In gaming scenarios, by perceiving the tilt angle of the phone, they realize steering control in racing games with high sensitivity and no obvious delay. Barometric pressure sensors can detect altitude changes in real time. Combined with GPS positioning, they can improve positioning accuracy in complex terrains such as mountains and high-rise buildings, with an error generally between 5m and 10m. Touch pressure sensors can adjust the thickness of lines according to the pressing force in drawing apps, restoring the real drawing experience. In daily operations, long-pressing icons with different forces can trigger different shortcut functions, enriching interaction methods. Capacitive touch sensors support wet-hand touch. When fingers are wet with water or slightly sweaty, they can still maintain a touch success rate of over 80%. Ultrasonic touch technology further improves touch accuracy when wearing gloves.
However, acceleration sensors and gyroscopes are prone to cumulative errors during long-term intense motion (such as running and hiking), leading to high step-counting data. Especially when going up and down stairs, due to complex motion postures, the error rate may rise to 10%~15%. Barometric pressure sensors are greatly affected by weather. In extreme weather such as rainstorms and typhoons, atmospheric pressure fluctuates severely, leading to inaccurate altitude detection and thus affecting positioning accuracy. The pressure recognition range of touch pressure sensors is narrow. For users sensitive to force, there may be cases where the force distinction is not obvious. Moreover, long-term pressing of the screen will cause sensor fatigue, resulting in decreased pressure detection sensitivity. When there are oil stains or a thick layer of water on the screen surface, the touch success rate of capacitive touch sensors will significantly decrease, and even touch failure may occur. Although ultrasonic touch technology can improve wet-hand touch, its cost is high and it has not yet been popularized in mid-to-low-end models.
Temperature Sensors
Mainly used to monitor body temperature and ambient temperature, ensuring smartphone heat dissipation control, battery safety, and environmental perception needs in certain scenarios. Common types include thermocouple sensors, thermistor sensors, and infrared temperature sensors.
Thermocouple sensors measure temperature through the thermoelectric potential of two different metals, featuring a wide measurement range and fast response speed. They are mostly used to monitor the temperature of core components such as smartphone processors and batteries. Thermistor sensors work using the characteristic of semiconductor materials whose resistance changes with temperature. They are small in size and low in cost, mainly used for ambient temperature detection. Infrared temperature sensors measure temperature by receiving infrared radiation emitted by objects, enabling non-contact temperature measurement. Some models integrate them near the camera to measure human body surface temperature.
Their measurement range is generally -50℃ to 150℃, with an accuracy of ±1℃ and a response time within 100ms, which can quickly capture temperature changes of core components. The measurement range of thermistors is -20℃ to 85℃, with an accuracy of ±0.5℃, suitable for routine ambient temperature detection. The infrared temperature measurement range is 30℃ to 45℃ (for human body temperature measurement), with an accuracy of ±0.3℃ to avoid body temperature measurement errors, and the response time is between 200ms and 500ms.
Thermocouple sensors are used to monitor processor temperature in real time. When the temperature exceeds 80℃, the heat dissipation system is triggered (such as fan startup or performance throttling) to avoid processor damage due to overheating. When monitoring battery temperature, if the temperature exceeds 45℃, the fast charging function will be paused to prevent battery swelling or fire. Thermistor sensors can feed back ambient temperature in real time, providing local temperature data in weather apps with a small error. Some models combine ambient temperature to adjust screen color temperature to improve visual comfort. Infrared temperature sensors can achieve non-contact human body temperature measurement during the epidemic, with a short measurement time and no need for additional equipment, facilitating daily use by users.
Because thermocouple sensors are installed inside the smartphone, close to the processor and battery, they are easily affected by local heat sources and may not accurately reflect the overall temperature distribution of the body. Moreover, long-term high-temperature operation will lead to a decrease in the accuracy of the sensor itself, requiring regular calibration. The measurement range of thermistor sensors is narrow and cannot adapt to extreme high or low-temperature environments. For example, in cold regions below -20℃, abnormal measurement data may occur. Meanwhile, their response speed is slow. When the ambient temperature changes rapidly (such as walking from an air-conditioned room to the outdoors), it takes 1s to 2s to update the data. Infrared temperature sensors are greatly affected by ambient light. Under strong direct light, infrared reception is easily interfered with, leading to increased body temperature measurement errors. Moreover, the measurement distance is limited, generally needing to be maintained between 5cm and 10cm. Too far or too close will affect accuracy. In addition, different skin colors and clothing occlusion may also lead to measurement result deviations.
Magnetic Field Sensors
Mainly used to perceive the Earth's magnetic field to realize functions such as compass and navigation direction calibration, and can assist other sensors in improving the accuracy of motion perception. The common type is the Hall effect sensor.
Hall effect sensors realize magnetic field perception by detecting the Hall voltage generated by magnetic field changes, featuring small size, low power consumption, and high sensitivity, making them the mainstream choice for smartphone magnetic field sensors. Some high-end models will use three-axis Hall sensors, which can simultaneously detect the magnetic field strength in the X, Y, and Z directions to improve the accuracy of direction recognition.
Its core parameters are measurement range, sensitivity, and resolution. The measurement range is generally ±1200μT (microtesla), which can cover the normal intensity range of the Earth's magnetic field from 25μT to 65μT. Sensitivity needs to reach 0.1μT/LSB to ensure the perception of weak magnetic field changes. The resolution needs to reach 0.01μT to ensure the accuracy of direction calibration. In addition, the response time needs to be <10ms to avoid navigation direction update delay.
Magnetic field sensors can provide stable support for navigation and direction recognition: in compasses, they can achieve direction accuracy within ±1°. Even in indoor weak magnetic field environments, they can maintain basic direction accuracy by working with acceleration and gyroscopes. In navigation scenarios, they assist GPS positioning in calibrating motion direction. When the GPS signal is weak (such as in tunnels or between high-rise buildings), the navigation direction accuracy can be maintained for a short time through the fusion algorithm of magnetic field sensors and inertial sensors, with an error generally between 5° and 10°.
However, the ability to resist external magnetic field interference is poor. Metal objects (such as keys and metal phone cases) and electronic devices (such as computers and speakers) around the phone will generate local magnetic fields, causing compass direction deviation, and in severe cases, direction inversion may occur. In strong magnetic field environments such as subways and substations, they will completely lose calibration ability and fail to work normally. In addition, components such as the phone's own speakers and microphones will also generate weak magnetic fields. After long-term use, zero drift may occur in the sensor, requiring users to manually calibrate the compass regularly. Although three-axis Hall sensors can improve direction recognition accuracy, in complex magnetic field environments, multi-axis data is prone to interference superposition, which may instead reduce the accuracy of direction judgment.
Humidity Sensors
Used to detect the relative humidity of the environment around the phone, assisting weather monitoring and air conditioning control apps in achieving intelligent adjustment. Meanwhile, it can be combined with temperature sensors to improve the accuracy of apparent temperature calculation. The common type is the capacitive humidity sensor.
By absorbing moisture in the air through hygroscopic materials and changing the capacitance value to measure humidity, it features fast response speed, high accuracy, and small size, suitable for installation in the narrow space inside smartphones. Some sensors integrate temperature compensation functions to reduce the impact of temperature changes on humidity measurement.
Its core parameters are measurement range, accuracy, response time, and repeatability. The measurement range is generally 20% RH to 90% RH (relative humidity), which can cover the common humidity range in daily life. Accuracy needs to reach ±3%RH to ensure the accuracy of humidity data. The response time is within 5 seconds to timely reflect environmental humidity changes. Repeatability needs to be controlled within ±1%RH to avoid large deviations in multiple measurements.
Humidity sensors can provide effective data for environmental perception: in weather apps, they can feed back the current environmental humidity in real time, combining temperature data to calculate apparent temperature and help users understand the comfort level. In air conditioning control apps, they automatically adjust the dehumidification or humidification functions of the air conditioner according to humidity changes to improve indoor comfort. Some models will optimize the screen anti-fog function according to humidity data. When the environmental humidity exceeds 80%RH, they will reduce screen brightness and remind users to pay attention to screen fogging issues.
Its weakness is the limited measurement range, unable to accurately detect extreme low humidity <20%RH or extreme high humidity >90%RH environments, such as desert areas or rainstorm days, where measurement data saturation or anomalies may occur. The hygroscopic material of the sensor is easily polluted. If the phone is used in a dusty or oily environment for a long time, dust and oil will adhere to the sensor surface, affecting the moisture absorption capacity and leading to a decrease in humidity measurement accuracy, requiring regular cleaning and maintenance. In addition, the response time of humidity sensors is greatly affected by air flow. In enclosed spaces (such as elevators) or environments with slow air circulation, the response speed will slow down, unable to timely reflect humidity changes. Moreover, after long-term use, the hygroscopic material will age, leading to poor repeatability, and the sensor needs to be replaced to restore accuracy.
II. Core Functions, Application Scenarios, and Analysis of Typical Problems of Smartphone Smart Sensors
Relying on unique perception capabilities, different types of sensors are applied in various scenarios. Meanwhile, due to technical bottlenecks, environmental interference, and other factors, typical problems affecting user experience have arisen.
Acoustic Sensors: Sound Collection and Playback Functions, Covering Call and Audio Entertainment Scenarios
1. Core Functions
Using microphones to realize the collection and conversion of sound signals, and speakers to complete the restoration of electrical signals to sound, the core functions focus on "sound input and output," specifically including voice collection, environmental noise filtering, and audio playback, providing basic support for scenarios such as calls, recording, and music playback.
2. Application Scenarios
In daily call scenarios, microphones collect user voice and filter environmental noise (such as traffic sounds on the road and conversation sounds in the office) through noise reduction algorithms, transmitting clear voice signals to the other party. Speakers (earpieces) restore the other party's voice to ensure call clarity. High-end models achieve directional sound pickup through multi-microphone arrays, further improving call quality in noisy environments.
In audio entertainment scenarios, when playing music, speakers (external loudspeakers) restore the pitch, timbre, and stereo sound field of the music. Models supporting lossless audio decoding can present richer music details. When recording videos, microphones simultaneously collect ambient sound. Some models support stereo recording, making the video audio track more spatial. In karaoke apps, microphones collect user singing in real time, combined with echo cancellation algorithms to avoid howling caused by speaker sound feedback.
3. Typical Problems
Insufficient adaptation of noise reduction algorithms: In complex noise scenarios (such as concert venues and construction sites), the microphone noise reduction algorithm cannot accurately distinguish between voice and noise, easily causing "over-noise reduction," which filters out background noise but also weakens the clarity of the user's voice, resulting in blurred and distorted sound heard by the other party.
Limited speaker sound quality: Limited by the volume of the smartphone sound cavity, low-frequency performance is generally insufficient. When playing music that requires strong bass, such as rock and electronic music, it lacks a sense of shock. After long-term high-volume playback, the speaker diaphragm fatigues, resulting in degraded sound quality (such as muddy bass and broken treble), and even permanent damage.
Failures caused by waterproof and dustproof issues: The waterproof rating of sensors in slim models is relatively low (such as IP53). After water ingress, microphone pickup becomes blurred and speaker volume decreases. Repair requires disassembling the body, which is easy to cause secondary damage. Dust ingress will block the microphone sound hole, leading to reduced collection volume and affecting recording and calls.
Optical Sensors: Light Signal Perception and Imaging Functions, Supporting Imaging and Biometric Recognition Scenarios
1. Core Functions
Realizing the conversion of light signals to image signals through camera modules to complete imaging functions such as photography and video recording. Perceiving light intensity through ambient light sensors to assist screen brightness adjustment. Realizing non-contact light signal detection through infrared sensors to support biometric recognition functions such as facial recognition. The core is "the capture, analysis, and application of light."
2. Application Scenarios
(1) Imaging and photography scenarios: The main camera is responsible for daily photography. Large-size sensors increase light intake, presenting photos with true colors and rich details in the daytime, and optimizing low-light image quality through multi-frame synthesis at night. Ultra-wide-angle lenses are used for landscape and architectural photography to expand the field of view. Telephoto lenses achieve long-distance shooting through optical zoom (such as shooting the moon or stage performances). Macro lenses capture details of close objects (such as flower textures and insect wings). When shooting videos, 4K resolution and HDR (High Dynamic Range) are supported. Some models also have cinematic modes, simulating background blur through depth calculation.
(2) Biometric recognition scenarios: In facial recognition unlocking, infrared sensors emit infrared light, and cameras collect infrared images of the face. Combined with algorithms to compare facial features, fast unlocking is achieved (generally within 0.3 seconds). In payment scenarios, liveness detection (such as blinking or head shaking verification) prevents forgery by photos and videos, ensuring payment security. Ambient light sensors, combined with facial recognition, automatically adjust the intensity of infrared fill light in strong or weak light to improve the recognition success rate.
(3) Smart screen adjustment scenarios: Ambient light sensors perceive external light (such as indoor lighting and outdoor sunlight) in real time, transmitting light intensity data to the system to automatically adjust screen brightness, avoiding the screen being too dark in strong light or too bright in weak light, reducing eye fatigue. Some models combine color temperature sensors to adjust screen color temperature (such as warm light and cool light) according to the color temperature of ambient light, improving visual comfort.
3. Typical Problems
(1) Scenario adaptation defects: When the telephoto lens is at high-multiple optical zoom (such as over 10x), hand shaking easily causes blurred images. Even with optical image stabilization enabled, stability is still insufficient when hand-holding to shoot distant objects. The focusing distance of the macro lens is too short (generally 2cm-5cm), making it difficult for users to master the focusing distance, easily causing "out of focus" and resulting in blurred detail photos. Large-size main cameras are often paired with large-aperture lenses. If the algorithm does not match the high-speed shutter in time when shooting moving objects, trailing is prone to occur, affecting the dynamic snapshot effect.
(2) Environmental limitations of facial recognition: In strong direct light (such as noon sunlight) or weak flickering light (such as candlelight) environments, infrared sensors are interfered with by light and cannot accurately collect facial infrared images, leading to unlocking failure. When users wear masks or sunglasses, facial features are occluded, and the algorithm cannot complete feature comparison, requiring switching to password or fingerprint unlocking. In low-temperature environments (such as below -10℃), screen touch sensitivity decreases, and fingers tapping the screen during facial recognition have no response, affecting unlocking efficiency.
(3) Ambient light misjudgment issues: In complex light scenarios (such as under tree shade or shopping mall spotlights), sensors easily misjudge local strong light as overall strong light, causing the screen brightness to suddenly increase, causing glare and discomfort. The installation position of sensors in some models is unreasonable (such as the top bezel of the screen being blocked by a phone case), which will continuously perceive "weak light," causing the screen brightness to be low for a long time, requiring manual adjustment by users.
Mechanical Sensors: Motion and Force Signal Perception Functions — Empowering Interaction and Motion Monitoring Scenarios
1. Core Functions
Perceiving linear motion (such as phone tilting and moving) through acceleration sensors, perceiving angular velocity (such as phone rotation) through gyroscopes, perceiving pressing force through pressure, and perceiving finger touch positions through touch. The core is "the perception of the phone's motion state, force conditions, and user touch operations."
2. Application Scenarios
(1) Interaction control scenarios: In screen rotation, acceleration sensors and gyroscopes collaboratively detect the phone's tilt angle (generally triggering rotation when exceeding 45°), automatically switching the screen from portrait to landscape (such as when watching videos or playing games), or from landscape back to portrait. In game interaction, gyroscopes perceive phone tilting (such as steering in racing games) and shaking (such as shaking dice games) to realize somatosensory operations. In touch interaction, capacitive touch sensors support multi-touch (such as pinching to zoom photos or three-finger screenshots). Pressure sensors realize long-press icon shortcut functions (such as pressing the WeChat icon to directly initiate a call) and line thickness adjustment in drawing apps by perceiving pressing force.
(2) Motion monitoring scenarios: In the step-counting function, acceleration sensors detect the up-and-down vibration of the phone (walking action), combined with algorithms to filter non-walking vibrations (such as bumps when riding in a car), and count daily steps, with an error rate generally around 5%. In sleep monitoring, acceleration sensors perceive users turning over and moving at night to judge sleep depth (light sleep, deep sleep) and generate sleep reports. Some models combine gyroscopes to improve the accuracy of motion posture recognition (such as distinguishing running, jumping rope, and climbing stairs).
(3) Assisted positioning scenarios: Barometric pressure sensors measure atmospheric pressure to calculate altitude, assisting GPS positioning. In scenarios where GPS signals are weak, such as mountains and high-rise buildings, positioning accuracy is improved (error reduced from 10 meters to within 5 meters). When users climb mountains or go downstairs, sensors update altitude data in real time, displaying the climbed height in sports apps.
3. Typical Problems
(1) Cumulative error in motion monitoring: Acceleration sensors and gyroscopes are prone to cumulative errors during long-term motion monitoring (such as continuous running for over 1 hour), leading to high step-counting data. For example, when going up and down stairs, because the vibration frequency is close to walking, the algorithm cannot completely filter it out, resulting in overcounting steps. When running on uneven roads (such as gravel roads), bump vibrations will be misjudged as walking actions, further expanding the error.
(2) Limitations of touch and pressure perception: When there are water stains or oil stains on the screen, the touch success rate significantly decreases, and even "drift" (deviation between the touch position and the actual click position) occurs. Touch basically fails with wet hands or when wearing thick gloves, unless the model supports ultrasonic touch. The force recognition range of pressure sensors is narrow (10g-400g). For users sensitive to force, it is difficult to precisely control the thickness of drawing lines. After long-term pressing, sensor sensitivity decreases, resulting in "no response to light pressing and triggering only with heavy pressing."
(3) Environmental limitations of positioning assistance: Barometric pressure sensors are greatly affected by weather. In extreme weather such as rainstorms and typhoons, atmospheric pressure fluctuates severely, leading to inaccurate altitude calculation and thus affecting GPS assisted positioning accuracy. In enclosed spaces (such as elevators or underground parking lots), pressure changes are not obvious, and sensors cannot effectively assist positioning, still relying on GPS signals, leading to positioning delay or failure.
Temperature, Humidity, and Magnetic Field Sensors: Environmental and Field Signal Perception Functions, Environmental Monitoring, and Navigation Scenarios
1. Core Functions
Realizing the monitoring of the temperature of core body components (processors and batteries), humidity sensors perceive environmental relative humidity, and magnetic field sensors perceive the Earth's magnetic field to realize direction recognition. The core is "the perception and analysis of environmental physical signals (temperature, humidity, magnetic field)."
2. Application Scenarios
(1) Device safety and heat dissipation scenarios: Real-time monitoring of processor temperature. When the processor overheats (exceeding 80℃) due to running large games or video editing software, the heat dissipation mechanism is triggered. Such as reducing the processor frequency (performance throttling) and starting the built-in fan of the body (in some gaming phones) to avoid processor burnout. Monitoring battery temperature. When the battery temperature exceeds 45℃ during charging, fast charging is paused to prevent battery swelling or fire.
The combination of temperature and humidity provides real-time temperature and humidity information in weather apps, assisting users in judging whether it is suitable for drying clothes or opening windows for ventilation. Some models use humidity sensors to detect the internal humidity of the body. If water ingress causes abnormal humidity, a waterproof warning is triggered to remind users to repair it in time.
(2) Navigation scenarios of magnetic field sensors: In map navigation, perceiving the Earth's magnetic field to determine the phone's orientation, combined with GPS positioning, realizes "the arrow synchronously pointing to the destination as the phone turns," improving the accuracy of walking navigation. In scenarios with weak GPS signals such as indoors and tunnels, magnetic field sensors assist in maintaining navigation direction and reducing positioning deviation.
3. Typical Problems
(1) Temperature response delay: When the phone suddenly runs high-load tasks (such as opening a large game), the temperature sensor needs 1-2 seconds to perceive the soaring processor temperature, leading to delayed startup of the heat dissipation mechanism, brief processor overheating, and potentially affecting performance stability.
(2) Humidity environment interference: In humid environments such as bathrooms and kitchens, humidity sensors are easily affected by condensed water, resulting in "false water ingress reports." After long-term use, dust accumulation on the sensor surface will lead to lower humidity detection values, affecting the data accuracy of weather apps.
(3) Magnetic field signal interference: In metal-dense environments such as subways and elevators, or when close to magnets and wireless chargers, the magnetic field will be interfered with, causing the navigation arrow to "deviate." Some phone protective cases contain metal materials, which will continuously shield magnetic field signals, requiring users to remove the protective case to restore navigation accuracy.
III. Industrial Status, Scale, and Development Pattern of Smartphone Smart Sensors
Global Industrial Status: Accelerated Technological Iteration and Expanded Application Scenarios
1. Technological Development Level
Currently, global smartphone sensor technology has entered the stage of "high precision, multi-dimension, and low power consumption." In optics, Sony's IMX989 supports 200-megapixel shooting, combined with AI (Artificial Intelligence) algorithms to achieve dynamic range optimization, becoming a standard configuration for high-end flagship phones. In acoustics, Knowles' MEMS microphone SNR breaks through 70dB, supporting active noise cancellation and spatial audio technology. In mechanics, Bosch's six-axis Inertial Measurement Unit (IMU) achieves an accuracy of ±0.5°/h, empowering smartphone image stabilization and AR navigation functions. Meanwhile, sensor fusion technology has become a trend. For example, Apple's "Sensor Hub" architecture in iPhones links optical, mechanical, and magnetic field sensor data in real time, improving the accuracy of Face ID and motion tracking.
2. Industrial Pattern
After industry restructuring and optimization, the global market presents a situation of "head concentration and clear division of labor":
Core sensor suppliers: Sony (global market share of smartphone optical sensors exceeds 50%), Bosch (mechanical sensor market share is about 40%), and Knowles Electronics (MEMS microphone market share exceeds 30%) dominate the high-end market;
Module integrators: Sunny Optical, O-Film (domestic), and Sharp (Japan) are responsible for module packaging, docking with smartphone manufacturers' needs;
Terminal brand owners: Head smartphone manufacturers such as Apple, Samsung, and Huawei combine self-research and external purchase. Apple self-develops optical sensor chips, and Huawei jointly develops customized mechanical sensors with domestic manufacturers.
Domestic and Foreign Industrial Comparison: Domestic Catching Up in Mid-to-Low End, Gap Remains in High End
1. Technological Level
Foreign advantages: International enterprises lead in core chip design and material processes. For example, Sony's CIS (CMOS Image Sensor) chips use Stacked CMOS technology, with sensitivity 30% higher than traditional processes. Bosch's MEMS sensors use wafer-level packaging, reducing power consumption by 20%.
Domestic progress: Domestic substitution has been realized in the mid-to-low-end market. For example, OmniVision (OV) under Will Semiconductor has an optical sensor market share of over 20% in the thousand-yuan phone market, and GoerTek's MEMS microphone shipments rank second globally. In the high-end field, the IMX800 series optical sensors jointly developed by Huawei and Sony are close to Sony's flagship products in dynamic range and low-light shooting, but chip foundry still relies on TSMC (Taiwan Semiconductor Manufacturing Company Limited, China).
2. Market Level
Global market distribution: In 2024, the global smartphone sensor market size was about 32 billion US dollars (data source: Yole Développement), with optical sensors accounting for the highest proportion (about 45%), followed by mechanical sensors (25%), and acoustic, temperature/humidity, and magnetic field sensors accounting for 30% in total. The Asia-Pacific region (China, South Korea, Japan) contributes over 60% of revenue, mainly concentrated in the smartphone manufacturing supply chain.
Domestic market characteristics: In 2024, the domestic smartphone sensor market size was about 85 billion RMB (CCID Consulting data), with an annual growth rate of 12%, higher than the global average (8%). Benefiting from the rise of domestic smartphone brands (Xiaomi, OPPO, and vivo have a combined global market share of over 30%), domestic sensor manufacturers have seen rapid growth in orders. However, high-end product procurement is still dominated by Sony and Bosch, with a localization rate of about 35% (mid-to-low-end reaches 70%, high-end is less than 10%).
Industrial Scale: Steady Global Growth, High Domestic Growth
1. Global Market Size
From 2020 to 2024, the global smartphone sensor market size grew from 22 billion US dollars to 32 billion US dollars, with a Compound Annual Growth Rate (CAGR) of about 10%. In segmented fields, due to the increase in the number of smartphone cameras (flagship phones are generally equipped with 4-6 cameras) and pixel upgrades (transitioning from 100 megapixels to 200 megapixels), the scale of optical sensors increased from 8 billion US dollars in 2020 to 14.4 billion US dollars in 2024, with a CAGR of 18%. Driven by foldable screen phones, the scale of mechanical sensors increased from 5.5 billion US dollars to 8 billion US dollars, with a CAGR of 10%. Acoustic and magnetic field sensors have slower growth rates (CAGR of about 5%) due to mature functions.
It is predicted that from 2025 to 2030, the global market will maintain a growth rate of 8%-10%, and the scale is expected to exceed 50 billion US dollars by 2030. The main driving forces come from new scenarios such as AR/VR phones and smart cockpit linkage (data interoperability between smartphone sensors and automotive sensors).
2. Domestic Market Size
From 2020 to 2024, the domestic smartphone sensor market size increased from 58 billion RMB to 85 billion RMB, with a CAGR of about 9.8%, higher than the global average. Among them, the scale of optical sensors increased from 26 billion RMB to 38 billion RMB, with a CAGR of about 9.6%, mainly driven by the popularization of multi-camera solutions in domestic smartphones. Mechanical sensors increased from 14.5 billion RMB to 21 billion RMB, with a CAGR of about 9.8%. The increase in the penetration rate of foldable screen phones (domestic foldable screen phone shipments reached 15 million units in 2024, a year-on-year increase of 40%) is a key factor.
Driven by policies such as comprehensively "improving the localization rate of core components such as sensors," local governments (Shenzhen, Shanghai, Suzhou) have set up special funds to support the sensor industry. It is expected that the domestic market size will exceed 100 billion RMB in 2025 and approach 200 billion RMB in 2030, with the localization rate expected to increase to over 50%.
Development Pattern: Coexistence of Supply Chain Restructuring and Global Competition
1. Supply Chain Distribution
Upstream: Core sensitive chip design, materials, and equipment. Midstream: Sensor manufacturing and module packaging. Downstream: Smartphone brand owners and third-party applications. The current supply chain shows a trend of "upstream shifting to China." Domestic manufacturers have global competitiveness in the module packaging segment (Sunny Optical module global market share exceeds 25%), but upstream chip design and high-end manufacturing are still limited by foreign enterprises.
2. Competitive Pattern
International competition: International leading enterprises occupy the high-end market through technical barriers (over 10,000 patents) and economies of scale (annual production capacity exceeds 1 billion units), and continuously penetrate the mid-to-low-end market. For example, Sony launched affordable optical sensors, squeezing the space of domestic manufacturers;
Domestic competition: Head manufacturers expand their share through mergers and acquisitions (Will Semiconductor acquiring OmniVision) and joint R&D (Sunny Optical cooperating with Huawei to develop AR). Small and medium-sized manufacturers focus on segmented fields (such as Suzhou Memsensing focusing on MEMS acoustic sensors), forming a pattern of "head attacking, waist following up, and tail filling the gap."
3. Future Trends
(1) Technology end: Sensors will develop towards "miniaturization, intelligence, and multi-modal fusion." For example, developing flexible sensors based on carbon nanotubes (which can fit curved screens) and intelligent sensors integrating AI algorithms (processing data locally to reduce power consumption);
(2) Market end: Domestic manufacturers are accelerating high-end breakthroughs. It is expected that by 2028, Huawei, Will Semiconductor, etc., will launch high-end optical sensors comparable to Sony, and the localization rate in the high-end market will increase to 20%-30%;
(3) Supply chain end: China will improve the full chain of "chip design - manufacturing - packaging." For example, SMIC (Semiconductor Manufacturing International Corporation) is promoting the mass production of 28nm sensor chips, and YMTC (Yangtze Memory Technologies Corp., Ltd.) is developing sensor-storage integration solutions to reduce dependence on foreign upstream.
IV. Product Process and Technological Innovation Development Trends of Smartphone Smart Sensors
Product Form Innovation: Miniaturization, Integration, and Flexibility
1. Miniaturization Breakthrough: Size Compression Empowers Multi-Camera Layout
Sony's IMX999 sensor launched in 2025 reduces the size to 1/1.3 inch (traditional sensors of the same pixel level are mostly 1 inch) through "wafer-level packaging" technology, and the thickness is reduced to 4.5mm. Combined with Xiaomi 15 Ultra, it realizes "four cameras of the same size arrangement," reducing the body thickness by 2mm.
Bosch's new generation six-axis IMU sensor adopts a "3D stacking" structure, reducing the volume from 5mm×5mm to 3mm×3mm. It can be embedded in the hinge area of foldable screen phones to detect the screen bending angle in real time and optimize the folding life prediction algorithm.
2. Integrated Products: Multi-Sensor Integration Reduces Costs
Apple's self-developed "Sensor Fusion X" module integrates optical facial recognition, magnetic field, temperature, and humidity sensors into a 0.8cm² chip. Applied to the iPhone 16 series in 2025, it reduces motherboard occupancy by 30% compared to the previous generation and lowers power consumption by 15%.
Domestic Sunny Optical launched the "multi-mode optical module," integrating the main camera, ultra-wide-angle, and TOF sensors into the same module. By sharing the optical path, the number of lenses is reduced. Applied to the vivo X100 series, it reduces the area of the rear camera module of the phone by 20%.
3. Flexible Innovation: Adapting to Curved Screens and Wearable Linkage
The flexible optical sensor jointly developed by Samsung and LG uses polyimide (PI) substrate material, with a bending radius of up to 5mm. It will be applied to the curved secondary screen of the Galaxy Z Fold6 in 2026 to realize full-screen fingerprint recognition.
GoerTek launched a flexible MEMS microphone. Through a "folded diaphragm" design, it can be attached to the curved surface of the phone frame. Applied to the Huawei Mate 70 Pro, it improves call noise reduction while solving the problem of traditional microphones occupying internal space of the body.
Process Technology Innovation: Improving Performance and Mass Production Efficiency
1. Chip Manufacturing Process: Advancing from 28nm to 14nm
SMIC achieved mass production of 14nm sensor chips in 2025, applied to Will Semiconductor's OV50H optical sensor. Compared with the 28nm process, chip integration is doubled, and power consumption is reduced by 40%. Combined with ISP, it can support real-time AI semantic segmentation (such as automatically separating the subject and background during shooting).
Sony introduced "EUV (Extreme Ultraviolet) Lithography" technology at its Kumamoto factory in Japan for manufacturing the IMX900 series sensors. The line width accuracy reaches 7nm, allowing the single pixel size to shrink to 0.7μm. Under the same sensor area, more pixels can be accommodated (such as a 1-inch sensor achieving 400 megapixels).
2. Packaging Process: Reducing Signal Loss and Space Occupancy
Bosch adopts "SiP (System in Package)" technology to package mechanical sensors, signal processing chips, and PMIC (Power Management IC) in the same module. The BMI320 series IMU launched in 2025 reduces signal transmission delay from 10ms to 2ms, improving the real-time performance of smartphone image stabilization and AR navigation.
O-Film developed the "glass-silicon bonding packaging" process for optical sensor modules, reducing the use of glue in traditional packaging and lowering light refraction loss. This increases the light intake of the sensor by 15%. Applied to the Honor Magic 7 series, it improves low-light shooting effects.
3. Mass Production Process Optimization: Reducing Costs and Defect Rates
Sunny Optical introduced an "AI visual inspection" system to identify solder joint defects, lens offset, and other issues in real time during module assembly. The defect rate dropped from 3‰ to 0.5‰. In 2025, mass production efficiency increased by 20%, driving down the price of domestic sensor modules by 10%-15%.
Apple adopted a "modular assembly" process, pre-assembling sensor modules and motherboards into independent modules, reducing the final assembly process of smartphones. In 2025, the sensor assembly time for the iPhone 16 series was shortened from 18 seconds to 8 seconds, and mass production efficiency increased by 55%.
Functional Innovation: Evolving from Single Perception to Scenario-Based Intelligence
1. Deepening Health Monitoring Functions: Beyond Basic Physiological Indicators
The optical sensor equipped on Apple iPhone 16 Pro is upgraded through "PPG (Photoplethysmography)" technology, which can monitor vascular elasticity (pulse wave velocity) in real time. Combined with AI algorithms, it predicts the risk of hypertension with an error of less than 5mmHg and has passed the FDA Class II medical device certification.
The acceleration sensor of the Huawei Mate 70 series adds a "respiratory rate monitoring" function. By detecting the micro-vibrations of the body caused by chest heaving, combined with temperature and humidity sensor data, it judges the risk of sleep apnea, with a sensitivity reaching 85% of medical-grade equipment.
2. Expanding AR Scenario Functions: More Natural Virtual-Real Fusion
The TOF optical sensor used in Samsung Galaxy S25 improves the measurement distance accuracy to ±1mm. Combined with magnetic field sensors for spatial positioning, in AR shopping scenarios, it can restore the size and lighting effects of products in the real environment in real time (such as presenting real shadows with light changes when placing virtual furniture).
The optical sensor of Xiaomi 15 Ultra supports "ambient light spectrum analysis," which can identify the color temperature and color gamut in the scene. During AR navigation, it fuses virtual arrows with the color and texture of the real road, reducing visual abruptness and improving navigation safety.
3. Vehicle-Phone Linkage Function Innovation: Smartphones as an Extension of Smart Cockpits
Apple CarPlay added a "sensor sharing" function in 2025. The optical sensor of the iPhone can recognize the driver's facial expressions (such as fatigue and distraction), and the mechanical sensor detects the phone holding state (judging whether the driver is holding the phone), synchronizing to the car ECU to trigger seat vibration reminders or voice warnings.
In Huawei's HarmonyOS Connect system, the phone's magnetic field sensor can assist car navigation. In scenarios with weak GPS signals such as tunnels and underground garages, through data fusion between the phone and vehicle sensors, navigation positioning accuracy is maintained within 1m.
Future Development Trends: Technological Fusion Driving Terminal Evolution and Industrial Reshaping
1. Terminal Form Evolution: From Handheld Devices to Intelligent Perception Hubs
Currently, smartphone sensor innovation has not yet touched the ceiling of performance and scenario applications. Instead, it promotes the evolution of smartphones from a single straight-bar form to "multi-modal extension." In the future, smartphones may realize scenario-based intelligent switching of sensors through designs such as foldable screens and modular external connections. For example, daily mode enables built-in optical and mechanical sensors, medical scenarios externally connect professional blood glucose monitoring modules, and vehicle linkage scenarios link vehicle sensors to realize data sharing. Meanwhile, smartphones will no longer be isolated perception terminals but will integrate the perception capabilities of smart wearables, smart homes, and smart cars through sensor data interoperability protocols, becoming "personal intelligent perception hubs." With the technological maturity of devices such as AR glasses and smart bracelets, smartphones can link with device sensors through data interoperability protocols, existing in the form of a "distributed sensor network," breaking the current limitation of "a single terminal bearing all perception functions" and becoming the core connecting the physical and digital worlds.
2. Deepening Iterative Relationship: Two-Way Empowerment Closed Loop Between Sensors and Smartphones
The iteration of the two has surpassed the level of "hardware superposition," forming a positive cycle of "user demand traction - sensor technology breakthrough - smartphone scenario expansion." On the one hand, the demand for lightweight, high-precision, and low-power perception in smartphones continuously drives breakthroughs in sensor technologies such as 14nm chip processes, SiP system-level packaging, and flexible substrate materials. On the other hand, the improvement of sensor performance continuously spawns new smartphone application scenarios. For example, the improvement in TOF sensor accuracy promotes the popularization of AR virtual try-on, and the breakthrough in bioelectric sensors realizes non-invasive blood pressure monitoring, feeding back the differentiated competition of smartphones in the health and entertainment fields. In the future, this synergy will be further upgraded: sensors will develop from "fixed modules built into smartphones" to "internal and external collaborative expansion." Smartphones will support the access of third-party professional sensors (such as industrial-grade infrared temperature measurement sensors and agricultural environmental monitoring sensors) by opening sensor data interfaces, forming an ecological model of "basic perception + professional expansion," making smartphones a bridge connecting consumer-grade and industrial-grade perception needs.
3. Life and Industrial Impact: From Personalized Services to Deep Integration with the Real Economy
At the personal life level, sensor fusion innovation will promote the popularization of "imperceptible intelligent services." For example, by recognizing user emotional fluctuations in real time through optical sensors, combined with the routine habits perceived by mechanical sensors, smartphones can automatically adjust pushed content, screen color temperature, and volume, while linking smart homes to adjust ambient temperature and lighting brightness. In travel scenarios, smartphone sensors and vehicle sensors interchange data. According to the fatigue state perceived by the user's phone, the vehicle can be automatically guided to switch to autonomous driving mode and plan the nearest rest stop. At the industrial level, the technological spillover effect of smartphone sensors will accelerate the integration of consumer electronics and the real economy: transplanting the algorithms of smartphone image recognition sensors to industrial quality inspection equipment can reduce procurement costs for small and medium-sized enterprises; applying smartphone health monitoring sensor technology to portable medical equipment can help primary medical institutions realize real-time monitoring of chronic diseases; and even extending the perception capability of smartphone vibration sensors to industrial equipment operation and maintenance, predicting equipment failures through vibration frequency analysis. Meanwhile, technological breakthroughs by domestic manufacturers in sensor chip design and optical module packaging will gradually break the monopoly of international enterprises such as Sony and Samsung, tilting the global intelligent perception industrial pattern towards "Intelligent Manufacturing in China," and realizing a comprehensive leap from technological innovation to industrial value.
Conclusion
In summary, the innovation of smartphone smart sensors in product form, process technology, and functional application is continuously breaking through and reshaping the boundary of mobile terminal perception capabilities. From hardware breakthroughs in miniaturization and integration, to efficiency improvements in chip processes and packaging technologies, to the landing of scenarios such as health monitoring, AR interaction, and vehicle linkage, every innovation is closely linked to user needs and industrial trends.
In the future, as terminal forms evolve towards "intelligent perception hubs," sensors and smartphones form a "two-way empowerment closed loop," and technological spillover promotes the deep integration of consumer electronics and the real economy, smartphone smart sensors will no longer be limited to functional supplements for single devices. Instead, they will become a key link connecting personal life and industrial transformation, and will also become an indispensable accompanying mobile intelligent terminal in life. Driven by the dual forces of technological breakthroughs and ecological construction by domestic manufacturers, this field is expected to achieve a leap from "following" to "leading," providing new highlights for the global intelligent perception industry, and injecting continuous momentum into people's smart life and the digital transformation of society.
(The next issue of this column will launch the fourth article on "Consumer Electronics Sensors", stay tuned!)
Author | Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society and Executive Vice Chairman of the China Sensor and IoT (Internet of Things) Industry Alliance
Editor | Yang Pengyue Art Editor | Ma Liya Supervisor | Zhao Chen