Let’s Give Them Something to Taco ‘Bout: Enabling Self-Driving Food Delivery With Postmates
It’s a familiar feeling. The day inches towards dinner and your stomach makes a simple, direct plea to your heart: You want tacos. That hand pressed corn tortilla, the tangy salsa and fresh cilantro — you’re getting hungry already!
But what if ordering tacos did more than just satisfy the gremlin in your gut? What if it actually…shaped the future?
Ford is working with Postmates, an on-demand delivery platform, to operate a self-driving delivery service.
Research vehicles for our business pilots are designed to appear as self-driving, however, they are manually driven by an experienced driver. The focus of our research is on the first and last mile of the delivery experience. We are developing our self-driving technology in separate test vehicles.
Our Postmates pilot is currently underway in Miami and Miami Beach with more than 70 businesses participating, including local favorites like Coyo Taco. For residents in the area, when you order tacos — or almost anything, really — through Postmates, you may be given the option to have your items delivered by a self-driving research vehicle.
What does that mean? It’s easier to show than to tell, so let’s examine the future of food delivery enabled by self-driving technology.
Some things don’t change — when your meal is ready to be delivered, a restaurant employee will place it in the vehicle. (Surprise! You ordered tacos.)
We designed a Transit Connect for this pilot program with a locker system to secure your food and allow us to serve multiple customers on one delivery route.
Additionally, services like Postmates must deliver an assortment of products from sushi restaurants to hardware stores. Therefore, the rear and passenger-side lockers are different sizes to allow us to test optimal vehicle configuration. Ultimately, we are testing how businesses and consumers interact with a self-driving vehicle.
This Ford Transit Connect self-driving research vehicle features three lockers — one on the passenger side and two in the back — that can deliver both food and goods.
After the restaurant employee types his access code into the screen, one of the lockers will automatically open so that he can place the food inside. Each locker has two cup holders so that you don’t have to worry about losing half your beverage in transit.
When the vehicle arrives at its destination, the customer receives a text notification indicating the delivery is ready for pickup.
Upon meeting the vehicle at the curb, consumers enter an access code into the touch screen and the appropriate locker will open. Audio prompts direct the interaction and lights will illuminate the designated locker. We’re making interactions with the vehicle as easy as possible through various sensory technologies built into the Transit Connect.
This is our first self-driving research vehicle modified specifically to test a variety of interfaces — the touch screen, the locker system, the external audio system— to inform the design of our purpose-built self-driving vehicle that’s scheduled to arrive in 2021.
Ultimately, through our partnership with Postmates, we’re testing methods for efficient deliveries to help local businesses expand their reach and provide a seamless experience to customers.
If you have the opportunity to check out the self-driving experience, jump at the chance to contribute to the future of delivery. And equally important — don’t forget to enjoy your meal!
Chasing Test Escapes In IC Manufacturing
The number of bad chips that slip through testing and end up in the field can be significantly reduced before those devices ever leave the fab, but the cost of developing the necessary tests and analyzing the data has sharply limited adoption.
Determining an acceptable test escape metric for an IC is essential to improving the yield-to-quality ratio in chip manufacturing, but what exactly is considered acceptable can vary greatly by market segment — and even within the same market depending upon a specific use case or time frame. The goal has been to reduce the number of failures in the field as chips have become more complex and as they become an essential part for safety-critical and mission-critical applications, but the emphasis on quality has been creeping into other markets, as well.
In the 1990s, quality engineers set the limit for desktop and laptops at 500 defects per million (DPM). With volumes of 1 million units per week, a computer system company can easily detect escapes. Today, automotive OEMs are demanding 10 ppm for much more complicated devices, even though car makers may find it challenging to measure escapes at this DPM level. Finding those escapes involves a deeper look into data, which in turn requires investments in data management, data analysis tools, and the engineering effort needed to make this all work.
With every test-time reduction decision, test content deliberation, and responses to test escapes, engineering teams determining test content process must grapple with the constructive tension found in the yield/quality/cost triangle, which is required to determine the test content process. And fundamental to all of this is having enough good data.
“We have an interesting problem within the semiconductor industry in that generally production yields are extremely high, which means there just isn’t that much fail data,” said Keith Schaub, vice president of technology and strategy at Advantest America. “So how do you develop a model to detect failures when it almost never fails? That’s a difficult problem. You must come up with some creative data-blind techniques, where you try to get the model to look for something different than the norm or out of the ordinary.”
The primary driver of these predictive models to detect test escapes has been feedback from customers. The reason is that “out of the ordinary” failed parts may be perfectly good in a customer system. If test escapes go down and the yield impact to failing good parts is minimal, then the new test metric is good enough.
How much data do you need?
In responding to customer returns, product, quality, and yield engineers revisit the yield/quality/cost triangle tradeoffs. Quality issues need to be addressed, and if this means that some good parts get thrown out, the quality engineer generally deems this needs to be an acceptable loss to make the customer happy.
This isn’t as simple as it sounds. To begin with, yield is assessed in terms of percentage, while quality is measured in ppm.
Moreover, to effectively chase test escapes, engineers need enough production volume to have the feedback from the end customer’s system. The more escapes, the less production volume engineers need to determine whether an issue exists. From there, assessing whether a new test adequately screens for test escapes requires just enough volume. Those numbers do not have to be the same.
However, no test is perfect. All of them are likely to fail a few good dies or units. Those false negatives commonly are referred to as overkill. If the fallout from a new test is in the range of 100 ppm, a yield engineer won’t blink an eye. A 1,000 ppm could be the battle line upon which a yield engineer and quality engineer have arguments. Yet in response to a customer failure, the quality engineer normally will win. If the yield loss is too excessive, then the product engineer needs to investigate other possible tests to distinguish bad from good parts.
Ratio of bad to good parts failed
How many good parts get thrown out when you apply a test? You can only measure this if you bother to look.
A system test, or an engineering characterization level of a parameter, remains the final arbitrator of labeling a true failure. Consider two different real-world scenarios involving false negatives. The first revolves around a measurement of I/O timings. In comparing an ATE determination of pass/fail parts for timing measurements with the characterization of a bad part, it was found that a ratio of 1 true fail to 2 true good existed. The second involved implementing an outlier detection technique to detect escapes. The escapes measured on order of 100 ppm. The outlier detection technique caught the escapes and failed approximately two times good as measured by a system-level test. By coincidence both examples found a 1 fail to 2 good part ratio. For the second example 100 ppm fails results in an approximate 333 ppm total fails with 233 ppm as yield loss.
So how much data do you need to determine a test limit or predictive model that distinguishes between good and bad parts?
“The short and simple answer is, ‘How accurate do you want to be?’” said Jeff Roehr, IEEE senior member and 40-year veteran of test. “You can start implementing lot-based adaptive test limits after about 30 parts, if you can accept 10% error. The accuracy improves significantly (about 1% error) when the sample reaches 300 parts.”
These numbers assume a Gaussian distribution for the parameter of interest. Such errors would change if the distribution is bimodal, for example.
If engineers have previous product history to base their test methods on — i.e., always do static part average testing on this product — they can be comfortable with 30,000 units, which has an approximate error of 0.01%.
It’s not always necessary to have a large data set to verify the effectiveness of a new test screen. Engineers can have confidence even with smaller data sets if they have feedback from a customer system. What is required, though, are unique IDs.
Ken Butler, strategic business creation manager at Advantest America, highlighted the differences between large SoCs and analog products from his many years at TI. “With large SoCs, because we had die ID, we could track all the way through manufacturing. In our analog IC businesses, where we were also applying outlier test techniques, we didn’t have die ID in many cases, because the die are extremely small and you just can’t afford the die area to do it. So you have to run open loop, no feedback, and we applied such screens. In these situations, we typically used 100 wafers’ worth of data in order to be able to go through and determine some screens. Because not every IC product line has that much material, we did arrive at screens with less data. The concern is that if you create a screen based upon 5, 10, or 20 wafers’ worth of data, the likelihood that you’re going to see enough process variation is low. Then you’re likely to miss some defect mechanisms that you might otherwise catch with more data.”
The challenge, then, is that failures happen at such a low incidence that you need enough volume to discern they exist. Once you know they exist, you can study them and figure out what makes them different from good units. In the case of test escapes that impact the customer, the failures may seem random, which makes it seem impossible to determine a test screen.
Determining a test to detect escapes
For 100 ppm, a customer needs only a volume at min of 30,000 units, although 300,000 units provides engineers more confidence in the magnitude of the problem. This provides enough information to go into the detailed data analysis needed to determine “one of these things is not like the other.”
The number of publicly documented cases for how to manage test escapes is extremely limited. This is understandable, because such stories expose both the IC vendor and the end customer. But the value cannot be overstated. These cases provide the evidence that outlier detection testing works, even when engineers cannot find the physical evidence.
“In 2005 we were having a field return problem with a product that represented an escape of 100 ppm. Our analysis indicated that these field returns just simply didn’t work in the customer system, yet it passed all of our tests applied on ATEs,” said Roehr. “System-level test (SLT) was not part of our production flow and we couldn’t afford to add SLT. We did isolate the nature of the field return to know that an extensive engineering characterization could distinguish the field returns from parts that passed both SLT and ATE-based tests. We couldn’t afford the test time to run that engineering characterization type test on our ATE.”
So now the question is whether some other test parameter can be used to distinguish the field returns from good parts that passed system-level test?
“We started digging into the data,” Roehr said. “This is one of the first cases where we found that when you look at the parts on a wafer — lot-by-wafer-lot basis, or wafer-by-wafer basis — we can start to see something. If you looked at the part across the spec range, you don’t see a problem. But when you looked at the individual parts within a lot, there were a few parts in a lot that don’t look quite like their sisters even though these parts were well within spec.”
He noted that failure analysis on selected parts never determined a definitive defect mechanism, and he surmised that the change in behavior was due to a timing-related failure — a signal path with a bit more delay. In addition, a small sample of parts that failed the new test was run through the system test. Not all parts failed the system test, but enough of them failed to provide confidence there now existed a sufficient screen to detect all the field returns.
ROI for data collection and analytic platforms
Looking at all test data for a differentiator can provide engineers with a magnet to find needles in a haystack, which are the test escapes. Yet without adequate investment this may not be possible. With test escape stories similar to Roehr’s, other product engineers said it may take 9 to 12 months before they learn of a test escape issue. Then they need to delve into the test data archives. To do so with ease requires an investment in data collection, storage and analytics. In addition, due to data alignment issues and business barriers to data sharing, this is an easier task for product engineers at IDMs than at fabless companies.
“Segmented supply chains and the lack of data sharing are still general data management gaps to be overcome in the classic data flow: Customer design to foundry to OSAT to customer. To help address this today, we are seeing more “turnkey” manufacturing options for fabless customers,” said Mike McIntyre, director of software product management at Onto Innovation. “These build options help with data consolidation, but unfortunately these options are limited both in breadth of supported technologies, application, and number of participants.”
Semiconductor data analytic companies sell their yield management platforms to fabless companies, foundries, IDMs, and OSATs, because these customers want to understand their respective role in the IC performance and quality. Rarely can anyone upfront predict the new outlier detection techniques that will be needed for a product.
The question engineering managers ask their teammates wanting to invest in outlier detection upfront is, “What’s the return on investment?” It’s a challenge to know this up front with no prior engineering experience showing the value. The cost side of the yield/quality/cost test triangle comes up. Managers want to know what money will they save if their team spends the engineering effort to find outliers up front? Another question engineers will ask is how they will know these outliers are true failures, when it’s nine to twelve months for feedback from system application.
Industry sectors with products that have safety concerns may warrant an upfront identification of potential outlier tests. Risk mitigation has a return on investment. For large SoCs going into computing systems and ASIC devices with lower part volumes, it is harder to justify because the ROI is not clear.
“We can improve DPM by getting rid of outliers,” said Phil Nigh, R&D test engineer at Broadcom. “So let’s look at testing a typical SoC. How much DPM to detect by avoiding outliers? How much could it improve DPM? My experience has been maybe as much as 10%, and 10% is not a lot. I would say a lot of customers would not be able to measure that 10%.”
Test escapes from customer returns will continue to occur, and product, yield and quality engineers will need to respond. With today’s yield and test data analytic platforms assessing test data for possible outliers that may impact a customer system is now possible. Identifying them ahead of time seems pointless to most product engineers because they already are applying all known tests.
Test data analytic platforms can identify test parameter combinations that show distinct population differences. However, most engineers remain skeptical without proof that it fails in a customer system, and ultimately DPM only can be measured at the end customer system. Not all outliers will be indicative of a part that will fail a system.
Adaptive Test Gains Ground
Demand for improved quality at a reasonable cost is driving big changes in test processes.
Part Average Tests For Auto ICs Not Good Enough
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Lidar startup Innovusion closes $64M round led by Temasek
More investors are joining the wave to bet on lidar, the remote sensing method that uses laser light to measure distances and has garnered ample interest from automakers in recent times. But it’s also a technology that has long been scorned by Elon Musk partly due to its once exorbitant costs.
Innovusion, a five-year-old lidar company and a supplier to Chinese electric car upstart Nio, just landed a Series B funding round of $64 million. The new proceeds boost its total investment to over $100 million, not a small amount but the startup is in a race crowded with much bigger players that have raised hundreds of millions of dollars, like Velodyne and Luminar.
Temasek, the Singaporean government’s sovereign wealth fund, led Innovusion’s latest financing round. Other investors included Bertelsmann Asia Investment Fund, Joy Capital, Nio Capital, Eight Roads Ventures, and F-Prime Capital.
Innovusion runs core development teams out of Sunnyvale, California and Suzhou, an eastern Chinese city near Shanghai that the robotaxi unicorn Momenta also calls home.
Junwei Bao, Innovusion’s co-founder and CEO, is not deterred by the industry’s existing giants. Back at Baidu where Bao oversaw sensors and onboarded computing systems for autonomous driving, he also worked on the Chinese search engine leader’s investment in Velodyne.
“They were designing things more like a college student designing in their labs,” Bao said of Velodyne.
Lidar was a niche market up until about five years ago, the founder explained, for the technology was mostly used by a small community of amateurs and areas such as military, surveying and mapping. These were relatively small markets in terms of shipping volume and Velodyne filled the demand.
“They were not thinking about industrialization, volume manufacturing, or roadmap extensibility. They were a pioneer and we [Baidu] recognized their value… but we also knew their weakness.”
In fairness, Silicon Valley-based Velodyne today is a $2.2 billion company supplying to some of the world’s largest automakers, including Toyota and Volkswagen. It also pocketed a hefty sum of cash after going public via a SPAC merger last year. Innovusion’s strategy is to make sensors for automakers that are “good enough for the next five years,” according to Bao. The startup chooses “mature components” so it can quickly ramp up production to 100,000 units a year.
Its biggest customer at the moment is Nio, a Chinese challenger to Tesla which has backed Innovusion through its corporate venture fund Nio Capital. For mass production of its auto-grade lidar, Innovusion is partnering with Joynext, a smart vehicle arm of the Chinese auto component supplier, Joyson Electronics.
For now, China is the largest market for Innovusion. The startup is scheduled to ship a few thousand units this year, mainly for smart transportation and industrial use. Next year, it has a target to deliver several tens of thousands of units to Nio’s luxury sedan, ET7, which is said to have a scanning range of up to 500 meters, an ambitious number, and a standard 120-degree field of view.
Similar alliances between carmakers and lidar suppliers have played out in China as the former race to fulfill their “autonomous driving” promises with the aid of lidar. Xpeng, a competitor to Nio, recently rolled out a sedan powered by Livox, a lidar maker affiliated with DJI that markets its consumer-grade affordability.
Price is similarly important to Innovusion, which sells lidars to automakers for about $1,000 apiece at the volume of 100,000 per year.
“Adding a $1,000 upfront cost plus another couple thousand dollars for a car that’s selling for $30,000 or $50,000 is affordable,” Bao suggested.
With the fresh capital, Innovusion plans to increase the production volume of its auto-grade lidar and put more R&D efforts into smart cities and vehicles. The company has over 100 employees and plans to expand its headcount to over 200 this year.
Harley-Davidson spins out LiveWire into a standalone electric motorcycle brand
LiveWire, Harley-Davidson’s electric motorcycle, is being spun out as a standalone brand, complete with a new logo and brand identity.
Harley-Davidson first unveiled the LiveWire electric motorcycle in 2018 with a listing price of $29,799, placing it on the higher end for motorcycles. It went into production the following year, with some bumps, including a brief halt to production due to a charging-related problem on one of the motorcycles. The “first LiveWire-branded motorcycle” will launch on July 8. Its public debut will come a day later at the International Motorcycle Show, Harley-Davidson said Monday.
Dealers had trouble selling the bike to younger, newer motorcycle riders, Reuters reported in 2019. Part of the issue was the price, which is in the same category as a Tesla Model S, dealers told the news wire at the time. Given that Harley-Davidson’s core constituency is still baby boomers, who are beginning to age out of the products, the question is whether a new spin out and rebranding can attract younger (and affluent) riders.
The two companies will share technological advancements and LiveWire will “benefit from Harley-Davidson’s engineering expertise, manufacturing footprint, supply chain infrastructure, and global logistics capabilities,” Harley-Davidson said Monday.
LiveWire will have dedicated showroom locations, starting in California, and a “virtual” headquarters with hubs in Silicon Valley and Milwaukee.
Harley-Davidson is one of the most recognizable motorcycle makers in the country, but its sales have struggled in recent years. The company’s annual revenue dropped nearly 24% in 2020 compared to the previous year, though some of that is likely due to the economic effects of the coronavirus pandemic. The company also cut 700 jobs from its global operations last summer, in a restructuring plan known as “The Rewire.”
More recently, the company debuted a five-year strategic plan dubbed “The Hardwire.” Included in the plan is to further invest in the electric market. The company has already started moving in this direction with the release last November of its Serial 1 Cycle e-bicycles. Its Rush/Cty Speed model can hit speeds of up to 28 mph and comes in at $5,000.
NTSB: Autopilot could not have been engaged in fatal Tesla crash
Tesla’s advanced driver assistance system known as Autopilot could not have been engaged on the stretch of road where a Model S crashed last month in Texas, killing the two occupants, according to a preliminary report released Monday by the National Transportation Safety Board.
The results help clear up some of the mysteries around the crash, which has received widespread attention after police reported that there was no one in the driver’s seat, leading to speculation that Autopilot was functioning at the time.
Only adaptive cruise control, one of the functions in Autopilot, could be engaged in that section of the road, according to the NTSB. Autosteer, another feature that keeps the vehicle in the lane, was not available on that part of the road, the report says. The preliminary report supports comments made by Tesla’s vice president of vehicle engineering, Lars Moravy, who said during an earnings call that adaptive cruise control was engaged and accelerated to 30 miles per hour before the car crashed.
NTSB also confirmed there were only two occupants in the vehicle. When the two men were found, one was in the passenger seat and the other was in the back seat, which led to speculation about whether Autopilot was engaged and even conspiracy theories that there was a third occupant.
“Footage from the owner’s home security camera shows the owner entering the car’s driver’s seat and the passenger entering the front passenger seat,” the report reads. “The car leaves and travels about 550 feet before departing the road on a curve, driving over the curb, and hitting a drainage culvert, a raised manhole, and a tree.”
What is still unknown is whether the driver moved to another seat before or after the crash.
The NTSB said it will continue to collect data to analyze the crash dynamics, postmortem toxicology test results, seat belt use, occupant egress and electric vehicle fires. All aspects of the crash remain under investigation, the NTSB said.
The NTSB’s preliminary report also indicated that the crash of the Tesla Model S, which caught fire after hitting a tree, destroyed an onboard storage device and damaged the restraint control module — two components that could have provided important information about the cause of the incident. The car’s restraint control module, which can record data associated with vehicle speed, belt status, acceleration and airbag deployment, was recovered but sustained fire damage, the agency said. The NTSB has taken the restraint control module to its recorder laboratory for evaluation.
The NTSB is investigating the crash with support from Tesla and the National Highway Traffic Safety Administration. Harris County Texas Precinct 4 Constable’s Office is conducting a separate, parallel investigation.
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