Abstract: In accordance with some embodiments, a light detection and ranging (LiDAR) scanning system includes a light source. The light source is configured to transmit a pulse of light. The LiDAR scanning system also includes a beam steering apparatus configured to steer the pulse of light in at least one of vertically and horizontally along an optical path. The beam steering apparatus is further configured to concurrently collect scattered light generated based on the light pulse illuminating an object in the optical path. The scattered light is coaxial or substantially coaxial with the optical path. The LiDAR scanning system further includes a light converging apparatus configured to direct the collected scattered light to a focal point. The LiDAR scanning system further includes a light detector, which is situated substantially at the focal point. In some embodiments, the light detector can include an array of detectors or detector elements.

Abstract: An apparatus includes a chassis housing a control server compartment, a compute server compartment, and an input and output (IO) subsystem compartment. The apparatus further includes an IO subsystem inserted into the IO subsystem compartment, a compute server inserted into the compute server compartment, and a control server inserted into the control server compartment coupled to the compute server via an Ethernet connection. The IO subsystem includes one or more IO modules, where at least some of the IO modules can be coupled to sensors. The compute server receives the sensor data from the IO subsystem via some PCIe links and generates planning and control data based on the sensor data for controlling the autonomous vehicle. The control server controls and operates the autonomous vehicle by sending control commands to hardware of the autonomous vehicle based on the planning and control data received from the compute server.

Abstract: Disclosed is a method, computer method, system, and apparatus for measuring two-dimensional distributions of optical emissions from a plasma in a semiconductor plasma processing chamber. The acquired two-dimensional distributions of plasma optical emissions can be used to infer the two-dimensional distributions of concentrations of certain chemical species of interest that are present in the plasma, and thus provide a useful tool for process development and also for new and improved processing tool development. The disclosed technique is computationally simple and inexpensive, and involves the use of an expansion of the assumed optical intensity distribution into a sum of basis functions that allow for circumferential variation of optical intensity. An example of suitable basis functions are Zernike polynomials.

Abstract: Disclosed is a method and apparatus for measuring semiconductor substrate temperature using a differential acoustic time of flight measurement technique. The measurement is based on measuring the time of flight of acoustic (ultrasonic) waves across the substrate, and calculating a substrate temperature from the measured time of flight and the known temperature dependence of the speed of sound for the substrate material. The differential acoustic time of flight method eliminates most sources of interference and error, for example due to varying coupling between an ultrasonic transducer and the substrate. To further increase the accuracy of the differential acoustic time of flight measurement, a correlation waveform processing algorithm is utilized to obtain a differential acoustic time of flight measurement from two measured ultrasonic waveforms. To facilitate signal recognition and processing, a symmetric Lamb mode may be used as mode of excitation of the substrate.

Abstract: An IO subsystem chassis includes IO modules and IO slots to receive the IO modules inserted from a frontend of a housing, a baseboard disposed within the housing, the baseboard including first connectors corresponding to the IO slots to receive and connect the IO modules. Each of the IO modules can be coupled a server via the backend panel using a cable. Each IO module includes an IO card having a peripheral device mounted thereon and a card holder having a first receiving socket to receive and hold the IO card plugged in vertically and downwardly. The card holder further includes a second connector to engage with or disengage from a corresponding one of the first connectors of the baseboard horizontally, when the IO module is inserted into or removed from a corresponding IO slot from the frontend, without having to removing the housing.

Abstract: Embodiments are generally directed to neural network training for library-based critical dimension metrology. An embodiment of a method includes optimizing a threshold for a principal component analysis of a spectrum data set to provide a principal component value, estimating a training target for one or more neural networks, training the one or more neural networks based both on the training target and on the principal component value provided from optimizing the threshold for the principal component analysis, and providing a spectral library based on the one or more trained neural networks.

Abstract: Described is a method and system for measuring parameters of a structure on a substrate, such as a via or a through-silicon via (TSV) using an imaging X-ray metrology system. A previously-trained Support Vector Machine (SVM) model is used to extract structure parameters from the acquired structure X-ray images. Training of the Support Vector Machine (SVM) model is accomplished by using a library of actual or simulated X-ray images, or a combination of the two image types, paired with structure parameter sets.

Abstract: Described is a method and system for measuring parameters of a structure on a substrate, such as a via or a through-silicon via (TSV) using an imaging X-ray metrology system. A previously-trained Support Vector Machine (SVM) model is used to extract structure parameters from the acquired structure X-ray images. Training of the Support Vector Machine (SVM) model is accomplished by using a library of actual or simulated X-ray images, or a combination of the two image types, paired with structure parameter sets.

Abstract: Disclosed is an in-situ optical monitor (ISOM) system and associated method for controlling plasma etching processes during the forming of stepped structures in semiconductor manufacturing. The in-situ optical monitor (ISOM) can be optionally configured for coupling to a surface-wave plasma source (SWP), for example a radial line slotted antenna (RLSA) plasma source. A method is described to correlate the lateral recess of the steps and the etched thickness of a photoresist layer for use with the in-situ optical monitor (ISOM) during control of plasma etching processes in the forming of stepped structures.

Abstract: Disclosed is a method, computer method, system, and apparatus for measuring two-dimensional distributions of optical emissions from a plasma in a semiconductor plasma processing chamber. The acquired two-dimensional distributions of plasma optical emissions can be used to infer the two-dimensional distributions of concentrations of certain chemical species of interest that are present in the plasma, and thus provide a useful tool for process development and also for new and improved processing tool development. The disclosed technique is computationally simple and inexpensive, and involves the use of an expansion of the assumed optical intensity distribution into a sum of basis functions that allow for circumferential variation of optical intensity. An example of suitable basis functions are Zernike polynomials.

Abstract: Disclosed is a method and apparatus for measuring semiconductor substrate temperature using a differential acoustic time of flight measurement technique. The measurement is based on measuring the time of flight of acoustic (ultrasonic) waves across the substrate, and calculating a substrate temperature from the measured time of flight and the known temperature dependence of the speed of sound for the substrate material. The differential acoustic time of flight method eliminates most sources of interference and error, for example due to varying coupling between an ultrasonic transducer and the substrate. To further increase the accuracy of the differential acoustic time of flight measurement, a correlation waveform processing algorithm is utilized to obtain a differential acoustic time of flight measurement from two measured ultrasonic waveforms. To facilitate signal recognition and processing, a symmetric Lamb mode may be used as mode of excitation of the substrate.

Abstract: Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described.

Abstract: Disclosed is an in-situ optical monitor (ISOM) system and associated method for controlling plasma etching processes during the forming of stepped structures in semiconductor manufacturing. The in-situ optical monitor (ISOM) can be optionally configured for coupling to a surface-wave plasma source (SWP), for example a radial line slotted antenna (RLSA) plasma source. A method is described to correlate the lateral recess of the steps and the etched thickness of a photoresist layer for use with the in-situ optical monitor (ISOM) during control of plasma etching processes in the forming of stepped structures.

Abstract: Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described. In an example, a method includes optimizing a threshold for a principal component analysis (PCA) of a spectrum data set to provide a principal component (PC) value, estimating a training target for one or more neural networks, training the one or more neural networks based both on the training target and on the PC value provided from optimizing the threshold for the PCA, and providing a spectral library based on the one or more trained neural networks.

Abstract: Methods of generating wide process range libraries for metrology are described. For example, a method includes generating a first library having a first process range for a first parameter. A second library is generated having a second process range for the first parameter. The second process range is overlapping with the first process range. The second library is stitched to the first library to generate a third library having a third process range for the first parameter. The third process range is wider than each of the first and second process ranges.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.

Abstract: Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described.

Abstract: Methods of generating wide process range libraries for metrology are described. For example, a method includes generating a first library having a first process range for a first parameter. A second library is generated having a second process range for the first parameter. The second process range is overlapping with the first process range. The second library is stitched to the first library to generate a third library having a third process range for the first parameter. The third process range is wider than each of the first and second process ranges.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.

Abstract: Metrology data from a semiconductor treatment system is transformed using multivariate analysis. In particular, a set of metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. One or more essential variables for the obtained set of metrology data is determined using multivariate analysis. A first metrology data measured or simulated for one or more substrates treated using the treatment system is obtained. The first obtained metrology data is not one of the metrology data in the set of metrology data earlier obtained. The first metrology data is transformed into a second metrology data using the one or more of the determined essential variables.