(Summary: We inevitably get questions about Numetrics’ technology after webinars or live event presentations, and we’d like to share some of them in the spirit of helping you understand more about our products and solutions. Here are answers to several recent questions in the virtual mail bag).
Q: How do you define productivity?
A: We calculate complexity of the project and we divide the complexity units by total number of person weeks required to get that product out to volume production. That quotient gives you the productivity number. The typical range is 500 on the low end for a large team to 3000 for a small team.
There’s another measure, which is throughput, and throughput is complexity units per week. That’s a measure of normalized cycle team. Productivity is efficiency of the team and higher number is better.
Q: I’ve heard that in some sectors productivity decreases as team size increases. Is this true in semiconductor product development?
A: It’s a universal effect across pretty much any activity that has to do with building things. When you build larger teams, each person is doing a smaller and smaller slice of the overall work. More work has to be split apart and then put back together. Bigger teams equal more meetings and more management required. It’s universal and it’s inevitable. With the Numetrics approach, you can minimize this effect—decreasing productivity curve is flatter than it would otherwise be.
Q: It’s impossible to predict in a design project how many times customer requirements will change, when your EDA tools go buggy or if a key contributor leaves the team. So how do you quantify schedule risk with so many unpredictable variables?
A: The simple answer is our tools don’t predict things. You have a draw a line between statistical analysis and a crystal ball.
What Numetrics’ tools do is take your inputs of design parameters and measure them against the history of more than 1,500 design projects over eight generations of technology evolution (here’s a link to a demo of our tools). Using the data from those hundreds and hundreds of designs, this builds in realistic effort required to deal with those issues. It’s a way of contingency planning.
Think of it like yield modeling. You know that on each wafer a certain number of dice will fall out. Yield modeling doesn’t tell you which particle is going to hit which die and where. But they give you an accurate assessment of how your design will yield. Numetrics is like a yield model for project plans. It’s saying there’s a certain probability that if you’re going to try to achieve these targets, given what you’ve input you’re going to fail.
It allows you to make a quantitative assessments. It’s a probability model. It’s not a crystal ball.
Q: How does the complexity calculation model handle predictions for newer nodes, such as 45 and 32nm?
A: Numetrics’ IC Industry Database has collected information for eight technology generations. The technology shifts from one generation to another have been observed before. And what we’ve observed is that early users of technology nodes face considerably more complexity than later users of the same node, once the models and such are more stable. The equation has calibrated this effect which repeats from generation to generation. We’ve been able to model what the effect of the extra technology of a new node will be on a new design.
Q: Can your tools get data from existing sources or do I have to input it manually?
A: We’re dealing with milestones, staffing information and complexity information. Typically this information is copy-pasted from existing sources or customers are using XML import to get data into our tools.
(Alex is Numetrics’ director of professional services).
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