Prasad Chaparala is the Director of Reliability Engineering at Amazon Lab126 in Sunnyvale, California. He is responsible for reliability engineering of a broad range of consumer electronic devices such as Echo smart speakers, Kindle e-readers, Fire tablets, and Fire TV products. Prior to this, he was the Vice President of Product and Reliability Engineering at Alta Devices from 2010 to 2014. Before joining Alta Devices, he was with National Semiconductor for 14 years in various process and reliability engineering roles. He received a Ph.D in Reliability Engineering from the University of Maryland, College Park. He has contributed to more than 50 publications in international journals and conference proceedings and holds 18 US patents. He is a recipient of three Best Paper awards at IEEE International Reliability Physics Symposium (IRPS). Additionally, he was served as the General Chair for the 2014 IRPS and was a member of the Board of Directors for IRPS.
Building Reliable Products Guided by Customer Obsession
With the rapid proliferation of consumer IoT devices that are embedded into everyday life, building both affordable and reliable hardware that continues to meet the highest customer expectations is of paramount importance. This is particularly challenging for new applications where customer-use conditions can vary broadly and can be unpredictable. Unlike other established industries such as semiconductor, automotive or aerospace where widely accepted reliability standards exists, there are no industry reliability standards for consumer electronics devices. In-depth understanding of customer usage environments, patterns, and expectations is critical in deriving appropriate system-level reliability specifications and test methods in order to build reliable devices that surpass customer expectations. This talk will provide an overview of how system-level reliability requirements for devices such as the Amazon Echo and Fire TV products are defined by working backwards from customer needs. The talk will cover various advanced engineering approaches in defining reliability specifications and test methods through user surveys, statistical analysis and machine learning techniques and customer feedback.