Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

Abstract: A High Electron Mobility Transistor (HEMT) having a reduced surface field (RESURF) junction is provided. The HEMT includes a source electrode at a first end and a drain electrode at a second end. A gate electrode is provided between the source electrode and the drain electrode. A reduced surface field (RESURF) junction extends from the first end to the second end. The gate electrode is provided above the RESURF junction. A buried channel layer is formed in the RESURF junction on application of a positive voltage at the gate electrode. The RESURF junction includes an n-type Gallium nitride (GaN) layer and a p-type GaN layer. The n-type GaN layer is provided between the p-type GaN layer and the gate electrode.

Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

Abstract: A Drain Extended Tunnel FET (DeTFET) device is disclosed that outperforms state of art devices and can meet the requirements of High voltage/high power devices operating in the range of 5V-20V for System on Chip (SoC). The device comprises a P+ SiGe source with an N-type Si Epilayer sandwiched between SiGe source and the gate stack, which enables vertical tunneling of minority carriers from SiGe P+ source into N-Epi region under the influence of gate field. The area tunneling between SiGe source and Si Epi region breaks the barrier imposed by thermionic injection based carrier transport from source to channel, which exists in DeMOS devices known in the art. The disclosed device results in improved performance in respect of ON current, leakage, sub-threshold slope, breakdown voltage and RF characteristics making it attractive for SoC applications as compared to its state of the art counterparts.

Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

Abstract: The disclosure herein describes a system for generating embeddings representing sequential human activity by self-supervised, deep learning models capable of being utilized by a variety of machine learning prediction models to create predictions and recommendations. An encoder-decoder is provided to create user-specific journeys, including sequenced events, based on human activity data from a plurality of tables, a customer data platform, or other sources. Events are represented by sequential feature vectors. A user-specific embedding representing user activities in relationship to activities of one or more other users is created for each user in a plurality of users. The embeddings are updated in real-time as new activity data is received. The embeddings can be fine-tuned using labeled data to customize the embeddings for a specific predictive model.

Abstract: A computing system including one or more processors generates a topic set for a domain. A taxonomic evaluator is executed by the one or more processors to evaluate a set of category clusters generated from domain-specific textual data against a domain-specific taxonomic tree based on a coherency condition and to identify the category clusters that satisfy the coherency condition. The domain-specific taxonomic tree is generated from hierarchical structures of documents relating to the domain. Each identified category cluster is labeled with a label. A topic set creator is executed by the one or more processors to insert the labels of the set of identified category clusters into the topic set for the domain.

Abstract: A method, system and computer program product includes receiving data including parameters affecting recovery point objectives for a disaster recovery plan, analyzing the parameters and determining responsible factors behind recovery point objective deviation based on the analyzing.

Abstract: In some examples, feature and context based search result generation may include identifying, based on analysis of a query feature associated with a query context of a query, and an entity feature associated with an entity context of each entity of a plurality of entities, a reduced number of entities that match the query. Based on analysis of a further query feature and a further entity feature, further matching analysis of the query to the reduced number of entities may be performed. The query may be linked by a linking model to an entity of the reduced number of entities to generate a query and entity pair. Selection of an entity may be received, and a linked plurality of queries and entities may be searched. In this regard, search results may be generated and include a set of queries that is associated with the selected entity.

Abstract: The present invention proposes a set of impurity doping configurations for GaN buffer in an AlGaN/GaN HEMT device to improve breakdown characteristics of the device. The breakdown characteristics depend on a unique mix of donor and acceptor traps and using carbon as a dopant increases both donor and acceptor trap concentrations, resulting in a trade-off in breakdown voltage improvement and device performance. A modified silicon and carbon co-doping is proposed, which enables independent control over donor and acceptor trap concentrations in the buffer, thus potentially improving breakdown characteristics of the device without adversely affecting the device performance.

Abstract: A Drain Extended Tunnel FET (DeTFET) device is disclosed that outperforms state of art devices and can meet the requirements of High voltage/high power devices operating in the range of 5V-20V for System on Chip (SoC). The device comprises a P+ SiGe source with an N-type Si Epilayer sandwiched between SiGe source and the gate stack, which enables vertical tunneling of minority carriers from SiGe P+ source into N-Epi region under the influence of gate field. The area tunneling between SiGe source and Si Epi region breaks the barrier imposed by thermionic injection based carrier transport from source to channel, which exists in DeMOS devices known in the art. The disclosed device results in improved performance in respect of ON current, leakage, sub-threshold slope, breakdown voltage and RF characteristics making it attractive for SoC applications as compared to its state of the art counterparts.

Abstract: A method, system and computer program product includes receiving data including parameters affecting recovery point objectives for a disaster recovery plan, analyzing the parameters and determining responsible factors behind recovery point objective deviation based on the analyzing.

Abstract: The present disclosure provides an improved enhancement mode field effect transistor (FET) having an oxide (AlxTi1-xO) emulating p-type gate. The present disclosure provides a novel enhancement mode High Electron Mobility Transistor (HEMT) structure with AlxTi1-xO Gate Oxide Engineering as Replacement of p-GaN Gate. In an aspect, the present disclosure provides a hybrid gate stack that combines p-GaN technology with the proposed oxide for e-mode operation. The HEMT structure with AlxTi1-xO Gate oxide provides a threshold voltage tuning from negative to positive by changing p-doping composition. Using a developed p-type oxide, e-mode device shows ON current ˜400 mA/mm, sub-threshold slope of 73 mV/dec, Ron=8.9 ?mm, interface trap density <1010 mm?2eV?1 and gate leakage below 200 nA/mm at the OFF-state breakdown.

Abstract: A High Electron Mobility Transistor (HEMT) having a reduced surface field (RESURF) junction is provided. The HEMT includes a source electrode at a first end and a drain electrode at a second end. A gate electrode is provided between the source electrode and the drain electrode. A reduced surface field (RESURF) junction extends from the first end to the second end. The gate electrode is provided above the RESURF junction. A buried channel layer is formed in the RESURF junction on application of a positive voltage at the gate electrode. The RESURF junction includes an n-type Gallium nitride (GaN) layer and a p-type GaN layer. The n-type GaN layer is provided between the p-type GaN layer and the gate electrode.

Abstract: A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.

Abstract: The present disclosure relates to a Dual Fin SCR device having two parallel fins on which cathode, anode, n- and p-type triggering taps are selectively doped, wherein one Fin (or group of parallel Fins) comprises anode and n-tap, and other Fin (or group of parallel Fins) comprises cathode and p-tap. As key regions of the proposed SCR (anode and cathode), which carry majority of current after triggering, are placed diagonally, they provide substantial benefit in terms of spreading current and dissipating heat. The proposed SCR ESD protection device helps obtain regenerative feedback between base-collector junctions of two back-to-back bipolar transistors, which enables the proposed SCR to shunt ESD current. The proposed SCR design enables lower trigger and holding voltage for efficient and robust ESD protection. The proposed SCR device/design helps offer a tunable trigger voltage and a holding voltage with highfailure threshold.

Abstract: The present disclosure provides a superjunction based design for normally-OFF HEMT that has two key components: (i) a recessed high-K metal gate and (ii) a superjunction layer under the gate, which is embedded within the N-type GaN buffer layers and separated from recessed gate. Recess gate is to deplete the 2 DEG from the channel region (under the gate) when the transistor is under OFF state. The present disclosure provides a new, improved, efficient and technically advanced HEMT device which can provide higher breakdown voltage, when compared to designs available in the prior-art, without affecting the performance figure of merits. Further, the new HEMT device offers improved breakdown voltage as compared to ON-resistance trade-off, improved the short channel effects, improved gate control over channel, improved switching speed for a given breakdown voltage, and improved device reliability. Furthermore, the new HEMT device lowers gate-to-drain (miller) capacitance and is available at low cost.

Abstract: SCRs are a must for ESD protection in low voltage—high speed I/O as well as ESD protection of RF pads due to least parasitic loading and smallest foot print offered by SCRs. However, conventionally designed SCRs in FinFET and Nanowire technology suffer from very high turn-on and holding voltage. This issue becomes more severe in sub-14 nm non-planar technologies and cannot be handled by conventional approaches like diode- or transient-turn-on techniques. Proposed invention discloses SCR concept for FinFET and Nanowire technology with diffused junction profiles with sub-3V trigger and holding voltage for efficient and robust ESD protection. Besides low trigger and holding voltage, the proposed device offers a 3 times better ESD robustness per unit area.

Abstract: A computing system including one or more processors generates a topic set for a domain. A taxonomic evaluator is executed by the one or more processors to evaluate a set of category clusters generated from domain-specific textual data against a domain-specific taxonomic tree based on a coherency condition and to identify the category clusters that satisfy the coherency condition. The domain-specific taxonomic tree is generated from hierarchical structures of documents relating to the domain. Each identified category cluster is labeled with a label. A topic set creator is executed by the one or more processors to insert the labels of the set of identified category clusters into the topic set for the domain.

Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.