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New SEMI Member D-SIMLAB Optimizes Semiconductor Manufacturing Decision-Making, Cuts Related Costs

D-SIMLAB Technologies, a Singapore-based provider of simulation-based business analytics and optimisation software solutions, recently joined SEMI. I spoke with Peter Lendermann, the company’s co-founder and Chief Business Development Officer, about the company’s role in the smart manufacturing movement, how customers are benefiting from D-SIMLAB solutions, and what the future holds for smart manufacturing.


Ng: What is D-SIMLAB’s mission?

D-SIMILAB Lendermann 1Lendermann: Our mission is to develop, market, and deliver high-performance simulation-based decision support solutions that enable corporations to enhance their performance in a sustainable manner leading to significant cost savings. In particular, we focus on semiconductor manufacturing material flow planning and optimisation but also do business in aviation where we help customers optimise their spare parts support operations. What these two domains have in common are three important attributes: They are capital intensive, their underlying operations are complex, and operations are also heavily affected by random, i.e. unpredictable events, which makes both planning and execution of manufacturing operations very challenging.

D-SIMLAB is a spin-off from the Singapore Institute of Manufacturing Technology (SIMTech) under the Agency for Science Technology and Research (A*STAR). Our head office is in Silicon Island Singapore. We also have representations in Germany and the U.S. Most of our staff are industrial and computer engineers with up to 20 years of operations experience in their respective industry domain, as well as vast data analytics and software development capability.

Ng: What solutions does D-SIMLAB offer to optimise semiconductor manufacturing?

Lendermann: In the three-pillar smart manufacturing framework of Connect, Sense and Predict advocated by SEMI, our focus is on Predict though we emphasise the equal importance of the subsequent Act: Our solutions can Predict, for example, WIP waves or usage-based preventive maintenance due dates. But much more value-add can be realised once some decisions with regard to how to Act can be derived from such a prediction. The ability to pro-actively adjust action plans in a timely manner is essential to overcoming challenges arising from changing customer due dates, mix profile changes, untimely production line issues, and production capacity to be shared with R&D lots effectively, so that ultimately our customers can enhance capacity, reduce cycle times and improve the due-date performance of their factories.

D-SIMILAB logoTo that end, our D-SIMCON solution suite spans the full spectrum of decision-support tools required to forecast, manage and optimise material flow – from operational scheduling and dispatching, WIP forecasting and dynamic and static capacity planning all the way to specific applications for fab load mix optimisation or for the enhancement of the product/layer dedication and resist allocation in the lithography area. Our solutions are implemented in numerous 6-, 8- and 12-inch wafer fabs operated by both IDMs and foundries worldwide with capacity ranging from 40,000 to 200,000 wafers per month.

Ng: What are the key enablers of D-SIMLAB’s success?

Lendermann: Our success lies in deploying production-ready solutions for our customers, allowing them to extract immediate value. Our solutions enable the portrayal of many domain-specific characteristics such as queue time constraints or specific equipment behaviour, which is absolutely essential to generating operationally feasible plans or schedules in order to be able to Act in the best possible manner according to what has been Predicted. Moreover, we have modules for automatic generation, calibration and maintenance of the underlying capacity model, including resolution of data inconsistencies as well as verification and validation of the model, to allow near real-time responses to continuously changing operations. And the associated optimisation approaches focus on creating maximum possible value with as few iterations as possible and within minimum time through smart heuristics and parallel computing infrastructure – a paradigm that is as powerful as it is cost-effective.

Ng: What are a few of your more notable customer successes?

Lendermann: As a result of the first implementation of our novel, multi-objective based Scheduler cum Dispatcher, a tool capacity gain of 8%, a transportation capacity gain of 10%, and an operator workload reduction of 25% were concurrently realised at one of the critical equipment groups in our customer’s fab. At another set of equipment groups in the same fab, a 7% increase of lots within the critical queue time limiting area was achieved.

D-SIMILAB PQAnother use case we successfully realised is fine-tuning of Preventive Maintenance plans: Based on a seven-day lot arrival forecast at each equipment generated with our WIP Forecaster, a recommendation is made when PM would be best possible without causing too much disruption in the WIP flow. The effect of this synchronisation of the PM plan with material flow enabled a dramatic reduction of the average queue lengths at critical equipment groups and the associated cycle times without incurring any capacity loss.


D-SIMILAB chartReduction of average queue length as a result of synchronising preventive maintenance with material flow.


Ng: What challenges has D-SIMLAB been facing in the COVID-19 world?

Lendermann: Obviously, software delivery projects have become more challenging for the time being since our engineers cannot be on-site frequently. But it also turned out that more and more services can be delivered remotely, which has the nice side effect of making the services more cost-effective for customers. Overall, we are confident that our solid customer base will enable us to sail steadily through these challenging times.

Ng: Where does D-SIMLAB see the technological development heading?

Lendermann: In the future, enriching decision support and manufacturing execution solutions with machine learning and other AI techniques will be critical in reducing dependency on human experience. This path is essential to making manufacturing operations fully Industry 4.0-compliant. D-SIMLAB will certainly be at the forefront of this development.

Bee Bee Ng is president of SEMI Southeast Asia.  

Topics: semiconductor manufacturing , Smart Manufacturing , foundries , Industry 4.0 , Southeast Asia , Singapore , Coronavirus , semiconductor wafers , pandemic , COVID-19 , D-SIMILAB , material flow planning , material flow optimisation , simulation-based decision support , Silicon Island Singapore , capacity planning , IDMs , critical equipment , WIP forecasting




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