Renesas looks to AI-enabled fab optimisation
UK startup is working with Renesas in the US on simulations to improve the efficiency of fab process technologies.
Renesas is using Flexciton’s intelligent scheduling software to improve the efficiency of scheduling wafer processing in the diffusion area of its wafer fab.
There are hundreds of overlapping timing interdependencies between the different stage and wafer batch sizes in the diffusion area. Flexciton has developed mathematical optimisation technology with smart decomposition techniques to work out solutions to scheduling automatically rather than using heuristics and operator expertise.
The software generates an optimised production schedule within a few minutes by searching through billions of scenarios to select the best possible one, and also considers the knock-on effects that one change can have against all the other constraints in the fab.
“Flexciton was able to show us several specific decisions we could have done differently to improve batching and cycle time. We are pursuing a live trial of the Flexciton software,” said Jay Maguire, Engineer at Renesas in the US.
The software was run in a simulation environment that replicated the way that Flexciton’s scheduler would have run live at the Renesas fab. The results showed that a significant improvement in reducing timelink violations of 29% could be achieved. Additional improvements would be possible of a 22% reduction in the number of batches and an 11% reduction in queue time despite these two KPIs being conflicting.
Currently, most fabs have no knowledge of the arrival times for future lots so operators can sometimes wait unnecessarily to maximise a batch size, causing more wafers to queue and damaging productivity. Uniquely, the Flexciton scheduler can see how lots are moving in time and can thus optimise the trade-off between number of batches and queue time to achieve the impressive gains seen on these conflicted KPIs.
“The key differentiator of our approach is that our software has the intelligence to predict what may happen in the future based on the current state of a fab,” said Jamie Potter, Flexciton’s co-founder and CEO. “It searches for the best solution amongst billions of possibilities to continuously keep finding the optimal schedule that meets the KPIs to maximise a fab’s productivity and profitability. Humans and heuristics just can’t do that.”
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