A sensor is provided and includes a first control line; a first signal line; a first auxiliary line; a first detection electrode; a first detection switch connected to the first detection electrode, the first control line and the first signal line; and a first shielding electrode connected to the first auxiliary line, wherein the first shielding electrode is located to overlap the first signal line via an insulating film.

Apparatuses, systems, and methods are provided for ultrasonic fingerprint sensors that are able to be used to detect fingerprints from opposing sides of an apparatus, e.g., a smartphone with screens on both sides of the housing. Some implementations of such sensors may include, for example, two piezoelectric and sensor pixel layer assemblies coupled to a common controller. Other implementations of such sensors may include, for example, a single piezoelectric and sensor pixel assembly coupled with a controller configured to apply a range-gate delay to obtain fingerprint scans from either opposing side of an apparatus. Yet further implementations of such sensors may include acoustic masking layers to spatially filter ultrasonic waves propagating to either side of an apparatus.

Semiconductor package having self-aligned structure disclosed. Semiconductor package includes a semiconductor chip having an active area and at least one marginal area that is located around the active area, wherein at least one alignment bar is arranged on the marginal area and a top structure being arranged on the semiconductor chip and having a groove being formed on a bottom surface, wherein the groove is configured for accommodating the alignment bar.

A method for monitoring a mobile input device with a screen on which information can be displayed in a first pixel raster of image elements and which comprises a flat optical fingerprint reader and a second pixel raster of light-sensitive sensor elements. A fingerprint and fingerprint characteristics, comprising position of the finger on the screen are detected. The sensor elements detect the light intensity incident on them. The intensity levels are assembled into a static pattern of the fingerprint. The combination of the static pattern and the fingerprint characteristics are compared with a database. If the combination is in the database, a check is carried out whether an action is associated with this combination, which is then carried out, or whether no action is associated with this combination, whereupon a first standard action is carried out. If the combination is not stored, a second standard action is carried out.

A piezoelectric micromachined ultrasonic transducer (PMUT) device includes a layer of piezoelectric material that is activated and sensed by an electrode and a conductive plane layer. The conductive plane layer may be electrically connected to processing circuitry by a via that extends through the piezoelectric layer. One or more isolation trenches extend through the conductive plane layer to isolate the conductive plane layer from other conductive plane layers of adjacent PMUT devices of a PMUT array.

A display substrate includes a base, a plurality of light-emitting devices disposed on the base, an encapsulation layer disposed on a light-emitting side of the plurality of light-emitting devices away from the base, and at least one photosensitive sensor disposed on a surface of the encapsulation layer away from the base. Each of the at least one photosensitive sensor is configured to collect optical signals for texture recognition.

Provided herein are a calibration method for a fingerprint sensor and a display device using the calibration method, where, in the calibration method for a fingerprint sensor, the fingerprint sensor includes a substrate, a light-blocking layer located on a first surface of the substrate and having openings formed in a light-blocking mask, a light-emitting element layer located on the light-blocking layer and having a plurality of light-emitting elements, and a sensor layer located on a second surface of the substrate and having a plurality of photosensors; and the calibration method includes generating calibration data through white calibration and dark calibration, and applying offsets to the plurality of photosensors using the calibration data.

A fingerprint sensor includes a die, a plurality of conductive structures, an encapsulant, a plurality of conductive patterns, a first dielectric layer, a second dielectric layer, and a redistribution structure. The die has an active surface and a rear surface opposite to the active surface. The conductive structures surround the die. The encapsulant encapsulates the die and the conductive structures. The conductive patterns are over the die and are electrically connected to the die and the conductive structures. Top surfaces of the conductive patterns are flat. The first dielectric layer is over the die and the encapsulant. A top surface of the first dielectric layer is coplanar with top surfaces of the conductive patterns. The second dielectric layer covers the first dielectric layer and the conductive patterns. The redistribution structure is over the rear surface of the die.

The present disclosure relates to a method performed in a fingerprint analysis system for facilitating differentiating between a live finger and a spoof finger. The method comprises acquiring a plurality of time-sequences of images, each of the time-sequences showing a respective finger as it engages a detection surface of a fingerprint sensor. Each of the time-sequences comprises at least a first image and a last image showing a fingerprint topography of the finger, wherein the respective fingers of some of the time-sequences are known to be live fingers and the respective fingers of some other of the time-sequences are known to be spoof fingers. The method also comprises training a machine learning algorithm on the plurality of time-sequences to produce a model of the machine learning algorithm for differentiating between a live finger and a spoof finger.

The present disclosure relates to a method performed in a fingerprint analysis system for facilitating differentiating between a live finger and a spoof finger. The method comprises acquiring a plurality of time-sequences of images, each of the time-sequences showing a respective finger as it engages a detection surface of a fingerprint sensor. Each of the time-sequences comprises at least a first image and a last image showing a fingerprint topography of the finger, wherein the respective fingers of some of the time-sequences are known to be live fingers and the respective fingers of some other of the time-sequences are known to be spoof fingers. The method also comprises training a machine learning algorithm on the plurality of time-sequences to produce a model of the machine learning algorithm for differentiating between a live finger and a spoof finger.