Learn More
Multi-patterning (MP) is the process of record for many sub-10nm process technologies. The drive to higher densities has required the use of double and triple patterning for several layers; but this increases the cost of the new processes especially for low volume products in which the mask set is a large percentage of the total cost. For that reason there(More)
Integrating Directed Self Assembly (DSA) and Multiple Patterning (MP) is an attractive option for printing contact and via layers for sub-7nm process nodes. In the DSA-MP hybrid process, an optimized decomposition algorithm is required to perform the MP mask assignment while considering the DSA advantages and limitations. In this paper, we present an(More)
Co-development of design rules and layout methodologies is the key to successful adoption of a technology. In this work, we propose Chip-level Design Rule Evaluator (ChipDRE), the first framework for systematic evaluation of design rules and their interaction with layouts, performance, margins and yield at the chip scale (as opposed to standard cell-level).(More)
In sub-15 nm technology nodes, local metal layers have witnessed extremely high congestion leading to pin-access-limited designs, and hence affecting the chip area and related performance. In this paper, we assess the benefits of adding a buried interconnect layer below the device layers for the purpose of reducing cell area, improving pin access, and(More)
Manufacturing has been incapable of keeping up with Moore's law without significantly increasing process variability and imposing massive geometric restrictions on design. This paper highlights the design impact of variability and geometric constraints - including traditional design rules and pattern-scale constraints - and describes our approach for(More)
We introduce new approaches for augmenting annotated training datasets used for object detection tasks that serve achieving two goals: reduce the effort needed for collecting and manually annotating huge datasets and introduce novel variations to the initial dataset that help the learning algorithms. The methods presented in this work aim at relocating(More)
  • 1