Identifying Discordant Rhythms
2020-05-22 | 6 min read
You are seated comfortably in a magnificent theatre enjoying music from the orchestra in their best bib and tucker. The sweet sound of the music seeps through your ear drums. Suddenly, a discordant note breaks the methodical rhythm. Something must have gone wrong. You scan the orchestra, trying to filter out the source. It’s not easy looking over the sea of musicians, with so many notes played, to identify and determine the cause of disruption.
Let’s shift gear from occasional glitches that may mar the beauty of a symphony, to ‘listening out’ for potential catastrophic malfunctions from the cacophony of signals that can impact the connected car and it’s users’ safety. This spans from using drones to collect crowd control data in a national park, to high-speed data for vehicle-to-everything (V2X) systems designed for collision avoidance.
In the case of advanced driver assistance systems (ADAS), radar sensors in the forward collision warning system monitor the speed of the car, calculate the speed of the forward vehicle, and the distance between them. The system then sends a warning to the driver if both vehicles are too close. In such devices, you need repeated testing and simulation to ensure the reliability and quality of the sensor processor chips.
Prior to mass producing these processor chips, extensive validations like device characterization must be done. During device characterization, integrated circuit (IC) design engineers need to optimize the IC by looking at its behavior in response to different I/O signals from other components. An oscilloscope is a classic tool that helps engineers to scan the streams of signals.
The oscilloscope displays the change of an electrical signal over time, with voltage and time as the Y- and X-axes, respectively. Engineers typically run repeated tests over prolonged periods of time with different test settings.
This results in data accumulation of enormous file sizes. The engineer then needs to rely on his expertise and experience to identify possible defects based on analyzing the captured waveforms.
All this manual eyeballing of waveforms on numerous simulations may take hours, and sometimes up to days, and is prone to human error. The time and resources taken to complete the design validation phase is about 70% of the total time needed to develop most complex ASICs.
Recognizing the need to overcome these challenges, Keysight created a revolutionary solution called PathWave Waveform Analytics (PWA). This powerful software can help users efficiently and effectively identify anomalies among the huge amounts of waveform data.
The software is able to compress raw waveform data up to 60% from its original file size, with high resolution playback for analysis. The engineer only needs to carry out a one-time raw waveform data transfer to the solution architecture.
This tool helps to greatly reduce the cost of storage and management of huge amounts of waveform data. The enhanced product validation also ensures higher quality delivery. The reduction in time needed for analysis and enhanced validation processes, will greatly support faster time to market for the products.
The architecture of PathWave Waveform Analytics includes a workstation called an edge computer, which connects to a cloud-based or on-premise server. The user has to initiate raw waveform data ingestion by transferring those large number of files from the oscilloscope to the edge computer. The edge computer will then compress and perform grouping of waveform shapes. Processed waveform data will then be accessible via the server, allowing users to analyze by eye-balling the reduced amounts of waveforms data on their laptop anytime and anywhere in the world, with an internet connection.
Better analysis of the device performance will improve product quality, and help to achieve zero defect targets for shipped ADAS and collision avoidance products. This will help make better technology that prevents road traffic accidents.
I am a firm believer in the benefits of ADAS. I was almost involved in a car crash while driving in US last year. My eyes were off the road for a few seconds as I fiddled with the unfamiliar infotainment system of my rented car to search for a classical music station. The collision avoidance alarm alerted me just in time, with barely a few meters to spare before hitting the car in front. Without the flawless performance of that sensor chip, I would not have escaped without injury. And lesson learnt – all eyes on the road, and hands on the steering wheel, till I get behind the wheels of a Level 3 autonomous vehicle or beyond.
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