Beyond the potential for more comprehensive simulation Rosenbaum said machine learning-based modeling also offers several other benefits that should be attractive to companies, such as the ability to share models without revealing vital intellectual property (IP).
“Because behavior modeling only describes, say input/output characteristics, they don't tell you what's inside the black box. They preserve or obscure IP. With a behavioral model a supplier can easily share that model with their customer without disclosing proprietary information,” Rosenbaum explained. “It allows for the free flow of critical information and it allows the customer then to be able to design their system using that model from the supplier.”
Most integrated circuit manufacturers, for example, use Input/Output Buffer Information Specification (IBIS) models to share information about input/output (I/O) signals with customers, while also protecting IP. The problem, Rosenbaum said, is that IBIS models tell you absolutely nothing about the circuit design details.
“Where machine learning can help is to make models such as IBIS better,” Rosenbaum said. “IBIS models don't represent interactions between the multiple I/O pins of an integrated circuit. There's a lot of unintended coupling that current models can't replicate. But with more powerful methods based on machine learning for obtaining models, next-gen models may be able to capture those important effects.”
The other great benefit would be reduced time to market. In the current state of circuit design there's almost a sense of planned failure that eats up a lot of development time. “Many chips don't pass qualification testing and need to undergo a re-spin,” Rosenbaum said. “With better models we can get designs right the first time.”
Rosenbaum comes from a background in system level ESD, a world she said is built on trial and error and would benefit greatly from behavioral modeling. “[Design engineers] make a product, say a laptop, it undergoes testing, probably fails, then they start sticking additional components on the circuit board until it passes...and it wastes a lot of time,” she said. “They build in time to fix things, but it's often by the seat of one's pants. If we had accurate models for how these systems would respond to ESD we could design them to pass qualification testing the first time.”
The willingness and interest in machine learning-based behavioral models is there, but the hurdles are in the details. How do you actually do this? Today, machine learning finds itself being largely applied to image recognition, natural language processing, and, perhaps most ignominiously, the sort of behavior prediction that lets Google guess what ads it wants to serve you.
“There's only been a little bit of work in regards to electronics modeling,” Rosenbaum said. “We have to figure out all the details. We're working with real measurement data. How much do you need? Do you need to process or filter it before delivering