Robert F. Cook - Putnam Valley NY Eduardo Garcia - Newburgh NY Nancy A. Greco - Lagrangeville NY Stephen E. Greco - Lagrangeville NY Ernest N. Levine - Poughkeepsie NY
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H01L 2100
US Classification:
438692
Abstract:
The propagation of a crack from the surface of the dielectric layer of an integrated circuit, through to the underlying circuit elements, is prevented by controlling the interface between the outermost, dielectric layer or layers and the inner layer or layers of the integrated circuit construction. The interface is weakened so that a crack that encounters the interface is caused to propagate in a horizontal manner, along the interface, preventing propagation of the crack in a direction that would be harmful to the manufactured article. This is preferably accomplished with multiple layers of material, each of which is made capable of redirecting (deflecting) the crack. Deflection of the crack, and arrest of the deflected crack along the interface, is made possible by controlling the fracture resistance of the interface.
Integrated Circuit Having Crack Stop For Interlevel Dielectric Layers
Robert F. Cook - Putnam Valley NY Eduardo Garcia - Newburgh NY Nancy A. Greco - Lagrangeville NY Stephen E. Greco - Lagrangeville NY Ernest N. Levine - Poughkeepsie NY
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H01L 2358
US Classification:
257629
Abstract:
The propagation of a crack from the surface of the dielectric layer of an integrated circuit, through to the underlying circuit elements, is prevented by controlling the interface between the outermost, dielectric layer or layers and the inner layer or layers of the integrated circuit construction. The interface is weakened so that a crack that encounters the interface is caused to propagate in a horizontal manner, along the interface, preventing propagation of the crack in a direction that would be harmful to the manufactured article. This is preferably accomplished with multiple layers of material, each of which is made capable of redirecting (deflecting) the crack. Deflection of the crack, and arrest of the deflected crack along the interface, is made possible by controlling the fracture resistance of the interface.
Dynamic Use Of Artificial Intelligence (Ai) Models On An Autonomous Ai Enabled Robotic Device
- Armonk NY, US Raghu Ramaswamy - Bangalore, IN Nirmit V Desai - Yorktown Heights NY, US Dhiraj Joshi - Edison NJ, US Satish Rajani - Dewas, IN Nancy Anne Greco - Lagrangeville NY, US Shiva G - Nagar, IN Aakash Praliya - Kota, IN Wei-Han Lee - White Plains NY, US Luis Angel Bathen - Placentia CA, US Tova Roth - Woodmere NY, US Sujoy Kumar Roy Chowdhury - Kolkata, IN Prakriti Pritmani - Ahmedabad, IN Kay Murphy - Nebo NC, US Shilpa Shenai - Bengaluru, IN Arun Yashwant Ingale - Pune, IN Ajjay Ratnakar - Bangalore, IN Gwilym Benjamin Lee Newton - Winchester, GB
International Classification:
B25J 9/16 B25J 13/08
Abstract:
Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.
Fusing Multimodal Data Using Recurrent Neural Networks
Embodiments relate to a system, program product, and method for employing deep learning techniques to fused data across modalities. A multi-modal data set is received, including a first data set having a first modality and a second data set having a second modality, with the second modality being different from the first modality. The first and second data sets are processed, including encoding the first data set into one or more first vectors, and encoding the second data set into one or more second vectors. The processed multi-modal data set is analyzed, and the encoded features from the first and second modalities are iteratively and asynchronously fused. The fused modalities include combined vectors from the first and second data sets representing correlated temporal behavior. The fused vectors are then returned as output data.
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