Abstract: A multispectral inspection (MSI) device for analyzing an electronic item having a printed circuit board (PCB). An electronic power supply powers the electronic item in accordance with one or more test vectors. An optical imaging scanner, terahertz (THz) imaging scanner, and a functional imaging scanner are each operative to scan the electronic item. An electronic processor is programmed to scan the various scanners and control the power supply to acquire optical, THz, and functional images of the electronic item. The images are combined to form a standard three-dimensional (3D) signature and artificial intelligence (AI) classifiers are applied to the 3D signature to perform non-destructive analyses of the electronic item.

Abstract: A multispectral inspection (MSI) device for analyzing an electronic item having a printed circuit board (PCB). An electronic power supply powers the electronic item in accordance with one or more test vectors. An optical imaging scanner, terahertz (THz) imaging scanner, and a functional imaging scanner are each operative to scan the electronic item. An electronic processor is programmed to scan the various scanners and control the power supply to acquire optical, THz, and functional images of the electronic item. The images are combined to form a standard three-dimensional (3D) signature and artificial intelligence (AI) classifiers are applied to the 3D signature to perform non-destructive analyses of the electronic item.

Abstract: To validate an integrated circuit (IC), the IC is imaged by scanning an optical beam over the IC to optically inject carriers and measuring an output signal generated by the IC in response to the injected optical carriers. A comparison between the image of the IC and a reference image is computed, and suspect regions of the IC are identified based on the comparison. The reference image may be an image of a reference IC. In another approach, images of training ICs are acquired, and a deep learning algorithm is trained to transform corresponding training IC layouts to the images of the training ICs. The trained deep learning algorithm then transforms a layout of the IC to generate the reference image. The comparison may be computed by computing an error metric for each region corresponding to a standard cell in the reference image.

Abstract: In an approach to inspecting integrated circuits, a system includes a first detection system and a second detection system for measuring electroluminescent (EL) images from a device under test (DUT); and a controller. The controller is configured to: measure EL emissions from the DUT with the first and the second detection systems to obtain a first and a second EL test data; compare the first and the second EL test data to a reference model of a reference device, the reference model developed based on measured EL reference data, synthetic EL reference data, or a combination thereof obtained from the reference device or a reference design of the reference device; and determine whether the DUT is in accordance with the reference device, based at least in part on the comparison of the first and the second EL test data to the reference model of the reference device.

Abstract: The present disclosure provides a method for generating a spatially resolved netlist that includes generating a netlist based on integrated circuit (IC) layout data and standard cell library data, the netlist including cell and net definitions associated with the IC; determining position data for respective cells and nets based on the IC layout data; mapping the position data to respective cell and net definitions in the netlist; and generating a spatially resolved netlist that includes the mapped position data to respective cell and net definitions.

Abstract: The present disclosure provides a satisfiability modulo theory (SMT) modeling system that includes graphical representation circuitry to generate graphical data representing a circuit design; finite state machine (FSM) discovery circuitry to discover, based on a feedback loop of the circuit design, an FSM contained within the graphical data; SMT assertion generation circuitry to generate an SMT assertion set of the FSM, based on the combinatorial and/or sequential logic elements associated with the FSM; and SMT modeling circuitry to determine a behavior of the FSM by applying one or more logical functions to the SMT assertion set.

Abstract: An assembly for monitoring a semiconductor device under test comprising a mill configured to mill the device, a sensor configured to measure an electrical characteristic of the device, and a computer configured to determine the amount of strain in the device from the electrical characteristic when the mill is milling the device and detect an endpoint of milling at a circuit within the device. In use the endpoints of the milling process of the semiconductor device are detected measuring an electrical characteristic of the device with a sensor during milling determining the amount of strain in the device from the electrical characteristic and detecting an endpoint of the milling process within the device based on the amount of strain.

Abstract: A system for decontamination of patient care equipment and a system for decontamination of an interior space. In some embodiments, a decontaminant distributor is provided in an enclosure and coupled to a decontaminant generator system. The decontaminant generator system may include a decontaminant generator removably coupled to the decontaminant distributor at an exterior of the enclosure for allowing the decontaminant generator to be removed and connected to another decontaminant distributor associated with another system for decontamination of patient care equipment. An air agitation system may be provided in the enclosure for agitating air and decontaminant in the enclosure. In some embodiments, a portable system may be transported to an interior space for decontaminating the items in the space using hot-air decontamination.

Abstract: A multispectral inspection (MSI) device for analyzing an electronic item having a printed circuit board (PCB). An electronic power supply powers the electronic item in accordance with one or more test vectors. An optical imaging scanner, terahertz (THz) imaging scanner, and a functional imaging scanner are each operative to scan the electronic item. An electronic processor is programmed to scan the various scanners and control the power supply to acquire optical, THz, and functional images of the electronic item. The images are combined to form a standard three-dimensional (3D) signature and artificial intelligence (AI) classifiers are applied to the 3D signature to perform non-destructive analyses of the electronic item.

Abstract: A system and method for selecting a test vector for use in classification of electronic components. The system and method can quickly and automatically select a test vector that, when applied to a set of training electronic components, causes the electronic components to produce side channel information that can be used to build a classifier that accurately classifies a set of test electronic components.

Abstract: A system and method for selecting a test vector for use in classification of electronic components. The system and method can quickly and automatically select a test vector that, when applied to a set of training electronic components, causes the electronic components to produce side channel information that can be used to build a classifier that accurately classifies a set of test electronic components.