Abstract: Systems, devices, and methods for providing unique die patterning are provided herein. A wafer substrate can include a plurality of individual semiconductor dies and a plurality of incomplete dies arranged along an edge of the wafer substrate. Each of the individual semiconductor dies and each of the incomplete dies can include a bond pad array. Each of a subset of the incomplete dies can also include a marking that may be identical or generally similar to the bond pad array. The subset of the incomplete dies, each of which includes both the bond pad array and the marking, can serve as target sites for a die attach machine during an auto-referencing process. In some embodiments, the bond pad arrays and the markings on the subset of the incomplete dies are formed via a photolithography patterning process using the same reticle.

Abstract: Systems and methods for mitigating crack meandering, are disclosed herein. In some embodiments, the method includes forming a metallic layer over planned scribe regions of an upper surface of a wafer, then selectively patterning and/or etching the metallic layer to form a plurality of isolated lines over the planned scribe regions. The method can then include depositing a passivation material over the plurality of isolated lines. Adjacent isolated lines can be separated from each other by a small enough distance to disrupt the deposition process, thereby creating a gap in the passivation material between each of the adjacent isolated lines. The gaps and/or trenches formed in the top surface of the wafer by etching the passivation material through the gaps can help attract cracks during a stealth dicing process, thereby reducing the amount the cracks meander away from the planned scribe regions.

Abstract: The present disclosure is directed to systems and methods for performing reading comprehension with machine learning. More specifically, the present disclosure is directed to a Neural Symbolic Reader (example implementations of which may be referred to as NeRd), which includes a reader to encode the passage and question, and a programmer to generate a program for multi-step reasoning. By using operators like span selection, the program can be executed over a natural language text passage to generate an answer to a natural language text question. NeRd is domain-agnostic such that the same neural architecture works for different domains. Further, NeRd is compositional such that complex programs can be generated by compositionally applying the symbolic operators.

Abstract: Provided are computing systems, methods, and platforms for negative sampling in knowledge graphs with improved efficiency. A knowledge graph comprising entities and links between the entities can be obtained. A query computation graph comprising nodes and edges can be generated based on the knowledge graph. The nodes of the query computation graph can include anchor nodes, a root node, and intermediate nodes positioned in paths between the anchor nodes and the root node. A node cut of a query of the query computation graph can be determined and can include at least one node that cuts at least one path between each anchor node and the root node of the query computation graph. Negative samples can be identified by bidirectionally traversing the query computation graph in a first direction from the anchor nodes to the node cut and in a second direction from the root node to the node cut.

Abstract: The present disclosure is directed to systems and methods for performing reading comprehension with machine learning. More specifically, the present disclosure is directed to a Neural Symbolic Reader (example implementations of which may be referred to as NeRd), which includes a reader to encode the passage and question, and a programmer to generate a program for multi-step reasoning. By using operators like span selection, the program can be executed over a natural language text passage to generate an answer to a natural language text question. NeRd is domain-agnostic such that the same neural architecture works for different domains. Further, NeRd it is compositional such that complex programs can be generated by compositionally applying the symbolic operators.