An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

An artificial neural network-based method for selecting a surface type of an object includes receiving at least one object image, performing surface type identification on each of the at least one object image by using a first predictive model to categorize the object image to one of a first normal group and a first abnormal group, and performing surface type identification on each output image in the first normal group by using a second predictive model to categorize the output image to one of a second normal group and a second abnormal group.

An artificial neural network-based method for selecting a surface type of an object includes receiving at least one object image, performing surface type identification on each of the at least one object image by using a first predictive model to categorize the object image to one of a first normal group and a first abnormal group, and performing surface type identification on each output image in the first normal group by using a second predictive model to categorize the output image to one of a second normal group and a second abnormal group.

Systems and methods are provided for acquiring images of objects. Light of different types (e.g., different polarization orientations) can be directed onto an object from different respective directions (e.g., from different sides of the object). A single image acquisition can be executed in order to acquire different sub-images corresponding to the different light types. An image of a surface of the object, including representation of surface features of the surface, can be generated based on the sub-images.

Systems and methods are provided for acquiring images of objects. Light of different types (e.g., different polarization orientations) can be directed onto an object from different respective directions (e.g., from different sides of the object). A single image acquisition can be executed in order to acquire different sub-images corresponding to the different light types. An image of a surface of the object, including representation of surface features of the surface, can be generated based on the sub-images.

Methods and systems for determining information for a specimen are provided. Certain embodiments relate to bump height 3D inspection and metrology using deep learning artificial intelligence. For example, one embodiment includes a deep learning (DL) model configured for predicting height of one or more 3D structures formed on a specimen based on one or more images of the specimen generated by an imaging subsystem. One or more computer systems are configured for determining information for the specimen based on the predicted height. Determining the information may include, for example, determining if any of the 3D structures are defective based on the predicted height. In another example, the information determined for the specimen may include an average height metric for the one or more 3D structures.

A distance measurement device includes: a light source configured to emit visible illumination light; an imaging element configured to receive reflected light of the illumination light from an object; and a signal processing circuit configured to reduce the emission of the illumination light in a predetermined period, detect a timing when the reception of the reflected light at the imaging element is reduced due to the reduction of the illumination light, and measure a distance to the object on the basis of the detected timing.

A distance measurement device includes: a light source configured to emit visible illumination light; an imaging element configured to receive reflected light of the illumination light from an object; and a signal processing circuit configured to reduce the emission of the illumination light in a predetermined period, detect a timing when the reception of the reflected light at the imaging element is reduced due to the reduction of the illumination light, and measure a distance to the object on the basis of the detected timing.

An augmented reality device includes an illuminator, a camera, a memory, and a processor, wherein the processor is configured to execute one or more instructions stored in the memory to turn on a light source of the illuminator to obtain a first image from the camera, turn off the light source to obtain a second image from the camera, estimate depth information, based on the first image, and estimate posture information, based on the second image.