How do semiconductor companies plan for aging? There has never been a truly efficient solution, and according to this article, problems are getting worse. For example, electronics in cars continue to get more complex (and more safety critical). But cars are used in very different ways after being sold, and in very different climates.
When a device is used constantly in a heavy load model for aging, particular stress patterns exaggerate things. An Uber-like vehicle, whether fully automated or not, has a completely different use model than the standard family car that actually stays parked in a particular state a lot of the time, even though the electronics are always somewhat alive. There’s a completely different aging model and you can’t guard-band both cases correctly.
Aging is dealt with by heuristics, which typically add a “safety margin” to designs. But it’s not accurate, and leaves money (chip area = $ per chip) on the table.
Moreover, margin typically isn’t just one thing. It’s actually a stack.“The foundry, with the models that they give us, includes a little bit of padding to cover themselves,” said ANSYS’ Geada. “And then the library vendor adds a little bit of padding and nobody talks about what that is, but everybody adds up this stack of margin along the way. “
Source: Circuit Aging Becoming A Critical Consideration
But of course, the semicon industry has been dealing with emerging challenges like this for its entire existence. Each new problem starts at a low stage of knowledge, beginning with Stage 0 (nobody knows the problem exists) and usually ending at about Stage 6.
Self-driving cars may eventually work together to create nearly real-time maps. But we’re nowhere close to that now.
Source: The Key to Autonomous Driving? An Impossibly Perfect Map – WSJ
By now, Tesla’s manufacturing problems are completely predictable. See my explanation, after the break. At least Wall St. is starting to catch on.
Also in this article: Tesla’s gigafactory for batteries has very similar problems. That surprises me; I thought they had competent allies helping with batteries.
But one engineer who works there cautioned that the automated lines still can’t run at full capacity. “There’s no redundancy, so when one thing goes wrong, everything shuts down. And what’s really concerning are the quality issues.”
Source: Tesla employees say Gigafactory problems worse than known
My friend Don Norman wrote an op-ed this weekend calling for an FDA-like testing program before autonomous cars are put on the roads in the US. Clearly, some level of government approval is important. But I see lots of problems with using drug testing (FDA = Food and Drug Administration) as a model.
Here is an excerpt from a recent article about testing problems with Uber cars, which were the ones in the recent fatal accident. After the break, my assessment of how to test such cars before they are allowed on American roads.
Waymo, formerly the self-driving car project of Google, said that in tests on roads in California last year, its cars went an average of nearly 5,600 miles before the driver had to take control from the computer to steer out of trouble. As of March, Uber was struggling to meet its target of 13 miles per “intervention” in Arizona, according to 100 pages of company documents obtained by The New York Times and two people familiar with the company’s operations in the Phoenix area but not permitted to speak publicly about it.Yet Uber’s test drivers were being asked to do more — going on solo runs when they had worked in pairs.And there also was pressure to live up to a goal to offer a driverless car service by the end of the year and to impress top executives.
So Uber car performance was more than 100 times worse than Waymo cars?!
I will be co-teaching, with Parand Darugar and Paul Kedrosky, a course on starting a company. This 10 week course requires each team to develop a real project, to the level that they can test it with potential customers. (Beta test, approximately.)
Doing this in 10 weeks can be daunting, but it can be done. I used to teach a course on hardware product development, which required working physical prototypes and 2 long reports in 10 weeks. This year we expect little or no hardware. Instead we are looking for web-enabled, mobile-enabled, or other service ideas with little or no associated hardware. What you will sell is some form of a system that fulfills a need.
Part of entrepreneurship is looking for, and “sensing,” unmet needs. Do this in your everyday life, in your courses, as you watch people on screens. Here are a few ideas to suggest how little is needed. Please, add your own. It’s ok to list an unmet need without saying how to solve it.
Classroom feedback using sheets of paper
A visual barcode based version of the teaching “clicker” that many classrooms use to gather real-time student feedback. Students hold up a piece of cardboard, which the faculty member scans with a phone. https://www.plickers.com Why didn’t I think of that? Get rid of the student-purchased hardware, and probably increase reliability at the same time. (Although I’m sure it sacrifices some capabilities of the electronic clickers.)
Search my many sources of free books
My wife and I read a lot of books, still. In the last 5 years a number of access methods for e-books have come along. I can buy ebooks from Apple, Amazon, or others. I can also borrow them free from my local library, Amazon Prime, scribd, my employer (UCSD’s library) and probably some other services. Each service has its own catalog and its own ways of searching. Checking each of the free services is time consuming, and half the time none of them has what I want. Solve this pain! There are many ways to solve the pain, and the concept could be integrated with existing book services/systems in a variety of ways.
Accelerated Learning by Experimentation
In most technologies and most industries, experiments play a central role in organizational learning as a source of knowledge and as a check before changes are implemented. There are four primary types of experiments: controlled, natural, ad-hoc, and evolutionary operation. This paper discusses factors that affect learning by experimentation and how they influence learning rates. In some cases, new ways of experimenting can create an order of magnitude improvement in the rate of learning. On the other hand, some situations are inherently hard to run experiments on, and therefore learning remains slow until basic obstacles are solved. Examples of experimentation are discussed in four domains: product development, manufacturing, consumer marketing, and medical trials.
Keywords: Learning, Experimentation
Full published version: Bohn Accelerated Learning by Experimentation. in Learning Curves: Theory, Models, and Applications edited by Mohamad Y. Jaber, CRC Press, 2011.
Preprint version, through SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1640767.
This is a rewritten version of my 1987 paper about manufacturing. Although I never published that paper, it was very influential in the 1990s, including becoming the (plagiarized) framework of a book and several articles by Stefan Thomke. This book chapter expands and extends the original concepts, including new material from Michael Lapré.
It has always been clear that Musk does not understand high-volume manufacturing. Building rockets is very hard, but building 100,000 cars is very hard for a different reason! His predicted ramp rate was absurd. In the last 6 months, I think he has started to realize this.
Tesla has little chance of hitting its 5,000 weekly output during the fourth quarter. The chief reason: Its current production line can’t build vehicles at that rate unless it runs two 10-hour shifts seven days a week, which is
Source: Tesla | Hiccups Threaten to Slow Model 3 Launch | Industry content from WardsAuto
According to the article, Tesla has also deliberately ignored much of the accumulated wisdom about how to ramp in auto production. That might be OK for his second high-volume vehicle.
More details on Tesla’s ramp plans:
Tesla now has 2 choices, both bad:
- Go ahead and start building and shipping as fast as possible. The result will be multiple problems that require expensive hardware recalls.
- Add another 6? months to the schedule to run the as a pilot line for learning, rather than for volume. Expect zero salable output during that period. (As one of the comments said, they can give/sell those cars to employees.)
Added December 27: Tesla “still in manufacturing hell.”
Latest of many articles about Tesla manufacturing problems.
Here is a comment on that article: Musk needs to face up to having made a MAJOR mistake when he skipped some steps in the original manufacturing ramp-up.
He is probably also making another major mistake at present: adding new machines to the manufacturing process, before he has the existing machines working perfectly. This seems logical to people with no manufacturing experience, but it does not work. For one thing, it diverts his key resource, which right now is manufacturing engineers.