Abstract: A semiconductor memory package includes a substrate, a first stack of memory dies, and a second stack of memory dies. The substrate includes a top layer and a bottom layer. The first stack of memory dies is electrically coupled to the top layer of the substrate and includes a controller and a first number of memory dies. The second stack of memory dies is electrically coupled to the top layer of the substrate and includes a second number of memory dies greater than the first number of memory dies. An upper surface of the first stack of memory dies and an upper surface of the second stack of memory dies are substantially coplanar.

Abstract: A semiconductor memory package includes a substrate, a first stack of memory dies, and a second stack of memory dies. The substrate includes a top layer and a bottom layer. The first stack of memory dies is electrically coupled to the top layer of the substrate and includes a controller and a first number of memory dies. The second stack of memory dies is electrically coupled to the top layer of the substrate and includes a second number of memory dies greater than the first number of memory dies. An upper surface of the first stack of memory dies and an upper surface of the second stack of memory dies are substantially coplanar.

Abstract: A method is disclosed herein. The method includes dicing a wafer and applying a mask. The method includes spraying die bond material, at a first temperature, to a surface of the wafer and cooling the die bond material at a second temperature to at least partially solidify the die bond material. The method also includes removing the mask from the wafer through the die bond material. After the removing of the mask, the method includes curing the die bond material to form a die attach film layer on the wafer.

Abstract: A semiconductor device package includes a semiconductor device with a ball grid array having a first subset of solder balls composed of metallic solder, and a second subset of solder balls composed of a composite material that includes a polymer core surrounded by a solder layer. The solder balls of the second subset can have a lower elastic modulus than the solder balls of the first subset and resist cracking due to thermal stresses on the semiconductor device package. In one embodiment, at least a portion of the second subset of solder balls is located on the periphery of the ball grid array such that the first subset of solder balls may be surrounded, at least partially, by the second subset of solder balls.

Abstract: Techniques are described for managing tasks of a dynamic system with limited resources using a machine-learning and combinatorial optimization framework. In one embodiment, a computer-implemented method is provided that comprises employing, by a system operatively coupled to a processor, one or more first machine learning models to determine a total demand for tasks of a dynamic system within a defined time frame based on state information regarding a current state of the dynamic system, wherein the state information comprises task information regarding currently pending tasks of the tasks. The method further comprises, employing, by the system, one or more second machine learning models to determine turnaround times for completing the tasks based on the state information, and determining, by the system, a prioritization order for performing the currently pending tasks based on the total demand and the turnaround times.

Abstract: Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients.

Abstract: A method is disclosed herein. The method includes dicing a wafer and applying a mask. The method includes spraying die bond material, at a first temperature, to a surface of the wafer and cooling the die bond material at a second temperature to at least partially solidify the die bond material. The method also includes removing the mask from the wafer through the die bond material. After the removing of the mask, the method includes curing the die bond material to form a die attach film layer on the wafer.

Abstract: Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can perform a first machine learning process to learn patterns in patient flow data related to a set of patient identities and a set of operations associated with a set of medical inpatient units. The system can also perform a second machine learning process to detect abnormalities associated with the patterns in the patient flow data. Furthermore, the system can determine patient census data associated with a prediction for a total number of patient identities in the set of medical inpatient units during a period of time based on the patterns and the abnormalities. The system can also generate an alert for a user interface in response to a determination that the patient census data satisfies a defined criterion.

Abstract: Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients.

Abstract: Techniques are described for managing tasks of a dynamic system with limited resources using a machine-learning and combinatorial optimization framework. In one embodiment, a computer-implemented method is provided that comprises employing, by a system operatively coupled to a processor, one or more first machine learning models to determine a total demand for tasks of a dynamic system within a defined time frame based on state information regarding a current state of the dynamic system, wherein the state information comprises task information regarding currently pending tasks of the tasks. The method further comprises, employing, by the system, one or more second machine learning models to determine turnaround times for completing the tasks based on the state information, and determining, by the system, a prioritization order for performing the currently pending tasks based on the total demand and the turnaround times.

Abstract: Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can perform a first machine learning process to learn patterns in patient flow data related to a set of patient identities and a set of operations associated with a set of medical inpatient units. The system can also perform a second machine learning process to detect abnormalities associated with the patterns in the patient flow data. Furthermore, the system can determine patient census data associated with a prediction for a total number of patient identities in the set of medical inpatient units during a period of time based on the patterns and the abnormalities. The system can also generate an alert for a user interface in response to a determination that the patient census data satisfies a defined criterion.

Abstract: A semiconductor device, and a method of its manufacture, are disclosed. The semiconductor device includes a semiconductor die, such as a controller die, mounted on a surface of a substrate. A bridge structure is also mounted to the substrate, with the semiconductor die fitting within a trench formed in a bottom surface of the bridge structure. The bridge structure may be formed from a semiconductor wafer into either a dummy bridge structure functioning as a mechanical spacer layer, or an IC bridge structure functioning as both a mechanical spacer layer and an integrated circuit semiconductor die. Memory die may also be mounted atop the bridge structure.

Abstract: A semiconductor device, and a method of its manufacture, are disclosed. The semiconductor device includes a semiconductor die, such as a controller die, mounted on a surface of a substrate. A spacer layer is also mounted to the substrate, with the semiconductor die fitting within an aperture or a notch formed through first and second major opposed surfaces of the spacer layer. Additional semiconductor die, such as flash memory die, may be mounted atop the spacer layer.

Abstract: A semiconductor device, and a method of its manufacture, are disclosed. The semiconductor device includes a semiconductor die, such as a controller die, mounted on a surface of a substrate. A spacer layer is also mounted to the substrate, with the semiconductor die fitting within an aperture or a notch formed through first and second major opposed surfaces of the spacer layer. Additional semiconductor die, such as flash memory die, may be mounted atop the spacer layer.

Abstract: A semiconductor device, and a method of its manufacture, are disclosed. The semiconductor device includes a semiconductor die, such as a controller die, mounted on a surface of a substrate. A bridge structure is also mounted to the substrate, with the semiconductor die fitting within a trench formed in a bottom surface of the bridge structure. The bridge structure may be formed from a semiconductor wafer into either a dummy bridge structure functioning as a mechanical spacer layer, or an IC bridge structure functioning as both a mechanical spacer layer and an integrated circuit semiconductor die. Memory die may also be mounted atop the bridge structure.