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Based on the same deep learning techniques used for detecting cancer and for beating the world champion Go player, artificial intelligence platforms are destined to enable fully autonomous vehicles.
This talk will cover the details of artificial intelligence in the automotive environment, and how a deep learning "supercomputer in a box" within the car itself will enable the dream of an autonomous vehicle.
Attendance to this seminar will count towards professional development hours for IEEE, ASQ. (2nd Edition)," Mike Silverman and Adam Bahret have delivered what few have done before: a comprehensive yet succinct overview of the field of reliability engineering and testing.
Development of a truly autonomous car requires deep learning and artificial intelligence.
With deep learning, the vehicle can be trained to have super-human levels of perception in order to navigate more safely than humanly possible.
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However, approximately 95% of the reliability issues are actually related to the packaging rather than the die.
There are a variety of surfaces and interfaces, the integrity of which needs to be maintained at all times, in order for the die to be able to function reliably over its service life.
A calibration technique based on fitting to measured electrical characterization data is presented, along with correlation of the electrical characteristics to direct physical strain measurements.
The limited characterization or measurement capabilities for 3-D IC stacks and a strict "good die" requirement make this type of analysis critical in order to achieve an acceptable level of functional and parametric yield.
A simulation flow that provides an interface between layout formats (GDS II, OASIS), and FEA-based package-scale tools, is developed.