Methods and systems for reverse design of micro-nano structure based on a deep neural network. The method includes step 101 of acquiring initial data of a micro-nano structure according to the micro-nano structure to be reversely designed. The method also includes step 102 of inputting the initial data of the micro-nano structure into a trained optical parameter prediction model to obtain optical prediction parameters. The method further includes step 103 of evaluating the optical prediction parameters. The method also includes optimizing the initial data of the micro-nano structure, inputting the optimized data of the micro-nano structure into the trained optical parameter prediction model, and performing steps 102 and 103 again until the evaluation result of the optical prediction parameters obtained in a current iteration satisfies the preset condition. Through the method of the present application, the electromagnetic response calculation time of the reverse design is greatly shortened.

Various embodiments of the present technology provide for the ultra-high density heterogenous integration, enabled by nano-precise pick-and-place assembly. For example, some embodiments provide for the integration of modular assembly techniques with the use of prefabricated blocks (PFBs). These PFBs can be created on one or more sources wafers. Then using pick-and-place technologies, the PFBs can be selectively arranged on a destination wafer thereby allowing Nanoscale-aligned 3D Stacked Integrated Circuit (N3-SI) and the Microscale Modular Assembled ASIC (M2A2) to be efficiently created. Some embodiments include systems and techniques for the construction of construct semiconductor devices which are arbitrarily larger than the standard photolithography field size of 26×33 mm, using pick-and-place assembly.

One aspect of this description relates to a method for operating an integrated circuit (IC) manufacturing system. The method includes placing a first nano-sheet structure within a IC layout diagram. The first nano-sheet structure has a first width. The method includes abutting a second nano-sheet structure with the first nano-sheet structure. The second nano-sheet structure has a second width. The second width is less than the first width. The method includes generating and storing the IC layout diagram in a storage device.

One aspect of this description relates to a method for operating an integrated circuit (IC) manufacturing system. The method includes placing a first nano-sheet structure within a IC layout diagram. The first nano-sheet structure has a first width. The method includes abutting a second nano-sheet structure with the first nano-sheet structure. The second nano-sheet structure has a second width. The second width is less than the first width. The method includes generating and storing the IC layout diagram in a storage device.

Within a layout of a first surface in a flip chip configuration, a bump restriction area is mapped according to a set of bump placement restrictions, wherein a first bump placement restriction specifies an allowed distance range between a bump and a qubit chip element in a layout of the first surface in the flip chip configuration. An electrically conductive material is deposited outside the bump restriction area, to form the bump, wherein the bump comprises an electrically conductive structure that electrically couples a signal from the first surface and is positioned according to the set of bump placement restrictions.

A machine learning system receives a witness function that is determined based on an initial sample of a dataset comprising multiple pairs of stimuli and responses. Each stimulus includes multiple features. The system receives a holdout sample of the dataset comprising one or more pairs of stimuli and responses that are not used to determine the witness function. The system generates a simulated sample based on the holdout sample. Values of a particular feature of the stimuli of the simulated sample are predicted based on values of features other than the particular feature of the stimuli of the simulated sample. The system applies the holdout sample to the witness function to obtain a first result. The system applies the simulated sample to the witness function to obtain a second result. The system determines whether to select the particular feature based on a comparison between the first result and the second result.

This specification describes methods, systems and apparatus for simulating quantum optical systems. According to a first aspect of this disclosure, there is described a computer implemented method of simulating a quantum optical system, the method comprising: receiving properties of the quantum optical system; receiving properties of an electromagnetic pulse; determining a set of pseudospin equations based on the properties of the quantum optical system; determining initial values of electric field components and/or magnetic field components, and pseudospin components on a grid corresponding to a region of space comprising the optical system based on the pseudospin equations and the properties of the electromagnetic pulse; performing temporally shifted updating of the electric field components, the magnetic field components and the pseudospin components on the grid based on Maxwell's curl equations and the pseudospin equation to simulate time evolution of the quantum optical system under the electromagnetic pulse; wherein the grid comprises: a first set of grid points associated with the magnetic field components; a second set of grid points associated with the electric field and pseudospin components, wherein the second set of grid points is spatially offset from the first set of grid points.

A method of determining a device behavior, wherein the method includes using a first procedure. The first procedure includes discretizing a user specified nano-device structure for at least one quantum method. Additionally, the first procedure includes solving the at least one quantum method, thereby having a solution of the at least one quantum method. Moreover, the first procedure includes extracting a parameter out of the solution of the at least one quantum method. Next, the first procedure includes applying at least one approximate method to the user-specified nano-device structure using the parameter. The first procedure additionally includes solving the at least one approximate method to the user-specified nano-device structure using the parameter. The first procedure also includes extracting the device behavior of the user-specified nano-device structure. Next, the method of determining the device behavior includes iterating the first procedure until a condition is satisfied.

Various embodiments of the present technology provide for the ultra-high density heterogenous integration, enabled by nano-precise pick-and-place assembly. For example, some embodiments provide for the integration of modular assembly techniques with the use of prefabricated blocks (PFBs). These PFBs can be created on one or more sources wafers. Then using pick-and-place technologies, the PFBs can be selectively arranged on a destination wafer thereby allowing Nanoscale-aligned 3D Stacked Integrated Circuit (N3-SI) and the Microscale Modular Assembled ASIC (M2A2) to be efficiently created. Some embodiments include systems and techniques for the construction of construct semiconductor devices which are arbitrarily larger than the standard photolithography field size of 26×33 mm, using pick-and-place assembly.

A method for characterizing a fluctuation induced by single particle irradiation in a device. A plurality of devices varying in size are tested respectively before and after irradiation to obtain threshold voltage distribution, such that a threshold voltage fluctuation induced by irradiation is obtained and used to correct a process fluctuation model, so as to correct a design margin of the devices working under the irradiation.