A method of testing an integrated circuit on a test circuit board includes performing, by a processor, a simulation of a first heat distribution throughout an integrated circuit design, manufacturing the integrated circuit according to the integrated circuit design, and simultaneously performing a burn-in test of the integrated circuit and an automated test of the integrated circuit. The burn-in test has a minimum burn-in temperature of the integrated circuit and a burn-in heat distribution across the integrated circuit. The integrated circuit design corresponds to the integrated circuit. The integrated circuit is coupled to the test circuit board. The integrated circuit includes a set of circuit blocks and a first set of heaters.

Techniques regarding determining the temperature of one or more quantum computing devices are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a temperature component that can determine a temperature of a superconducting resonator based on a frequency shift exhibited by the superconducting resonator due to a change in kinetic inductance with a change in temperature.

Systems, methods and computer program products leveraging digital twin modeling and cognitive computing to predict lubrication replacement for a physical asset. Predictions of lubrication replacement consider one or more various parameters such as operating conditions, usage parameters, the surrounding environment, overall health and state of repair of the physical asset, lubricant properties and historically collected data from the physical asset (or similarly comparable assets). Timing for optimal lubrication replacement is identified using the collected data of the physical asset, along with historical data, to simulate changes in a state of lubricants and lubricated parts within a physical asset using digital twin modeling to make predictions how one or more actions upon the physical asset impact the health, stability and/or longevity of the lubricant's lifespan. Based on the simulation results, recommended action(s) suitable for increasing and optimizing the overall life of the lubrication are provided and/or implemented.

An optimization apparatus includes electronic circuits configured to perform calculating, for a plurality of annealing units to which respective states and respective, different temperatures are assigned, respective evaluation function values responsive to the respective states, and causing a transition in the states with probabilities responsive to the temperatures and the evaluation function values, exchanging the temperatures or the states between the plurality of annealing units with predetermined probabilities based on the temperatures and the evaluation function values, and changing a temperature of an annealing unit of interest, such that a probability of exchange between the annealing unit of interest and a first annealing unit situated next thereto on a lower temperature side in a sequence arranged in order of temperature approaches a probability of exchange between the annealing unit of interest and a second annealing unit situated next thereto on a higher temperature side in the sequence.

A non-transitory computer readable recording medium includes simulation data input into a computing device executing a simulation of a semiconductor device, wherein the simulation data includes part shape information describing shape and terminal information of the semiconductor device, logical model information describing operation and connection information of an element in the semiconductor device, and functional block information describing positional information of a functional block in the semiconductor device, and the computing device causes the part shape information, the logical model information, and the functional block information to correspond to each other to execute the simulation of the semiconductor device.

A method is used to design nuclear reactors using design variables and metric variables. A user specifies ranges for the design variables and target values for the metric variables. A set of design parameter samples are selected. For each sample, the method runs three processes, which compute metric variables to thermal-hydraulics, neutronics, and stress. The method applies a cost function to each sample to compute an aggregate residual of the metric variables compared to the target values. The method trains a machine learning model using the samples and the computed aggregate residuals. The method shrinks the range for each design variable according to correlation between the respective design variable and estimated residuals using the machine learning model. These steps are repeated until a sample having a smallest residual is unchanged for multiple iterations. The method then uses the final machine learning model to assess relative importance of each design variable.

A data storage device including, in one implementation, a number of memory die packages disposed on a substrate within the data storage device. Each memory die package has a die density that includes one or more memory dies. The die density of each memory die package is configured to provide an even thermal distribution across the number of memory die packages. The respective die densities of two memory of the die packages are different from each other.

A thermal analysis model includes an intermediate node that imitates an intermediate portion and a first thermal resistance connecting to the intermediate node, and imitates the terminal portions on both sides. A terminal portion inside node connected to the first thermal resistance is configured to imitate an inside area adjacent to the intermediate portion and serves as a starting point of a first heat dissipation path to the substrate. A terminal outside node is configured to imitate an outside area separated from the intermediate portion and adjacent to the inside area in the terminal portions and serves as a starting point of a second heat dissipation path to the substrate. A second thermal resistance connects the terminal portion inside node and the terminal portion outside node and is arranged parallel to a different element imitating a thermal resistance of an electrode layer in a surface of the substrate.

An electromigration (EM) sign-off methodology that utilizes a system for analyzing an integrated circuit design layout to identify heat sensitive structures, self-heating effects, heat generating structures, and heat dissipating structures. The EM sign-off methodology includes a memory and a processor configured for calculating adjustments of an evaluation temperature for a heat sensitive structure by calculating the effects of self-heating within the temperature sensitive structure as well as additional heating and/or cooling as a function of thermal coupling to surrounding heat generating structures and/or heat dissipating elements located within a defined thermal coupling volume or range of the heat sensitive structures.

The disclosure provides a method, an apparatus, and a storage medium for controlling heating system. The method includes: establishing an objective function and constraints for estimating system parameters of the heating system, in which the heating system includes nodes, pipelines and equivalent branches, the equivalent branch is configured to represent a heating resource or a heating load in the heating system, the system parameters include a resistance coefficient of each of the pipelines and equivalent branches, and a heat dissipation coefficient of each of the pipelines; solving the objective function based on the constraints to obtain the system parameters; modeling the heating system based on the obtained system parameters to obtain control parameters of the heating system; and controlling the heating system based on the control parameters.