Abstract: A pyram-shaped deep-sea pressure-resistance shell and a design method therefor. The shell comprises a conical shell, an annular combined shell, a cylindrical shell, a flange bolt, and a perforated thick plate; a bottom end of the conical shell is connected with a top end of the annular combined shell, the conical shell being in communication with an interior part of the annular combined shell; the perforated thick plate blocks the bottom end of the annular combined shell, the perforated thick plate and the annular combined shell being connected by means of multiple flange bolts; the cylindrical shell is disposed inside the annular combined shell, a lower end of the cylindrical shell being inserted in a gap between the annular combined shell and the perforated thick plate.

Abstract: A semiconductor device includes a semiconductor substrate having a first region and a second region, insulators, gate stacks, and first and second S/Ds. The first and second regions respectively includes at least one first semiconductor fin and at least one second semiconductor fin. A width of a middle portion of the first semiconductor fin is equal to widths of end portions of the first semiconductor fin. A width of a middle portion of the second semiconductor fin is smaller than widths of end portions of the second semiconductor fin. The insulators are disposed on the semiconductor substrate. The first and second semiconductor fins are sandwiched by the insulators. The gate stacks are over a portion of the first semiconductor fin and a portion of the second semiconductor fin. The first and second S/Ds respectively covers another portion of the first semiconductor fin and another portion of the second semiconductor fin.

Abstract: A micro electro mechanical system (MEMS) includes a circuit substrate comprising electronic circuitry, a support substrate having a recess, a bonding layer disposed between the circuit substrate and the support substrate, through holes passing through the circuit substrate to the recess, a first conductive layer disposed on a front side of the circuit substrate, and a second conductive layer disposed on an inner wall of the recess. The first conductive layer extends into the through holes and the second conductive layer extends into the through holes and coupled to the first conductive layer.

Abstract: Methods and systems for compensating systematic errors across a fleet of metrology systems based on a trained error evaluation model to improve matching of measurement results across the fleet are described herein. In one aspect, the error evaluation model is a machine learning based model trained based on a set of composite measurement matching signals. Composite measurement matching signals are generated based on measurement signals generated by each target measurement system and corresponding model-based measurement signals associated with each target measurement system and reference measurement system. The training data set also includes an indication of whether each target system is operating within specification, an indication of the values of system model parameter of each target system, or both.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: Methods and systems for determining band structure characteristics of high-k dielectric films deposited over a substrate based on spectral response data are presented. High throughput spectrometers are utilized to quickly measure semiconductor wafers early in the manufacturing process. Optical models of semiconductor structures capable of accurate characterization of defects in high-K dielectric layers and embedded nanostructures are presented. In one example, the optical dispersion model includes a continuous Cody-Lorentz model having continuous first derivatives that is sensitive to a band gap of a layer of the unfinished, multi-layer semiconductor wafer. These models quickly and accurately represent experimental results in a physically meaningful manner. The model parameter values can be subsequently used to gain insight and control over a manufacturing process.

Abstract: Methods and systems for determining band structure characteristics of high-k dielectric films deposited over a substrate based on spectral response data are presented. High throughput spectrometers are utilized to quickly measure semiconductor wafers early in the manufacturing process. Optical models of semiconductor structures capable of accurate characterization of defects in high-K dielectric layers and embedded nanostructures are presented. In one example, the optical dispersion model includes a continuous Cody-Lorentz model having continuous first derivatives that is sensitive to a band gap of a layer of the unfinished, multi-layer semiconductor wafer. These models quickly and accurately represent experimental results in a physically meaningful manner. The model parameter values can be subsequently used to gain insight and control over a manufacturing process.

Abstract: Methods of precisely analyzing and modeling band gap energies and electrical properties of a thin film are provided. One method includes: obtaining a substrate and a thin film disposed above the substrate, the thin film including an interfacial layer above the substrate, and a high-k layer above the interfacial layer; determining a thickness of the thin film; analyzing the thin film using deep ultraviolet spectroscopy ellipsometry to determine the photon energy of reflected light; using a model to determine a set of bandgap energies extracted from a set of results of the photon energy of the analyzing step; and determining at least one of: a leakage current from a main bandgap energy, a nitrogen content from a sub bandgap energy, and an equivalent oxide thickness from the nitrogen content and a composition of the interfacial layer.

Abstract: A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Abstract: Methods and systems of process control and yield management for semiconductor device manufacturing based on predictions of final device performance are presented herein. Estimated device performance metric values are calculated based on one or more device performance models that link parameter values capable of measurement during process to final device performance metrics. In some examples, an estimated value of a device performance metric is based on at least one structural characteristic and at least one band structure characteristic of an unfinished, multi-layer wafer. In some examples, a prediction of whether a device under process will fail a final device performance test is based on the difference between an estimated value of a final device performance metric and a specified value. In some examples, an adjustment in one or more subsequent process steps is determined based at least in part on the difference.

Abstract: Methods and systems for determining band structure characteristics of high-k dielectric films deposited over a substrate based on spectral response data are presented. High throughput spectrometers are utilized to quickly measure semiconductor wafers early in the manufacturing process. Optical models of semiconductor structures capable of accurate characterization of defects in high-K dielectric layers and embedded nanostructures are presented. In one example, the optical dispersion model includes a continuous Cody-Lorentz model having continuous first derivatives that is sensitive to a band gap of a layer of the unfinished, multi-layer semiconductor wafer. These models quickly and accurately represent experimental results in a physically meaningful manner. The model parameter values can be subsequently used to gain insight and control over a manufacturing process.

Abstract: The present invention includes generating a three-dimensional design of experiment (DOE) for a plurality of semiconductor wafers, a first dimension of the DOE being a relative amount of a first component of the thin film, a second dimension of the DOE being a relative amount of a second component of the thin film, a third dimension of the DOE being a thickness of the thin film, acquiring a spectrum for each of the wafers, generating a set of optical dispersion data by extracting a real component (n) and an imaginary component (k) of the complex index of refraction for each of the acquired spectrum, identifying one or more systematic features of the set of optical dispersion data; and generating a multi-component Bruggeman effective medium approximation (BEMA) model utilizing the identified one or more systematic features of the set of optical dispersion data.

Abstract: Methods and systems of process control and yield management for semiconductor device manufacturing based on predictions of final device performance are presented herein. Estimated device performance metric values are calculated based on one or more device performance models that link parameter values capable of measurement during process to final device performance metrics. In some examples, an estimated value of a device performance metric is based on at least one structural characteristic and at least one band structure characteristic of an unfinished, multi-layer wafer. In some examples, a prediction of whether a device under process will fail a final device performance test is based on the difference between an estimated value of a final device performance metric and a specified value. In some examples, an adjustment in one or more subsequent process steps is determined based at least in part on the difference.

Abstract: The present invention includes generating a three-dimensional design of experiment (DOE) for a plurality of semiconductor wafers, a first dimension of the DOE being a relative amount of a first component of the thin film, a second dimension of the DOE being a relative amount of a second component of the thin film, a third dimension of the DOE being a thickness of the thin film, acquiring a spectrum for each of the wafers, generating a set of optical dispersion data by extracting a real component (n) and an imaginary component (k) of the complex index of refraction for each of the acquired spectrum, identifying one or more systematic features of the set of optical dispersion data; and generating a multi-component Bruggeman effective medium approximation (BEMA) model utilizing the identified one or more systematic features of the set of optical dispersion data.