$350 H100 replaces 40,000 CPUs! TSMC applies NVIDIA platform to accelerate computing and trigger a semiconductor manufacturing revolution Ali Guerra | usagoldmines.com

In the course of the “NVIDIA AI Summit” held in Washington on Tuesday, October eighth, NVIDIA emphasised the achievements of its collaboration with Taiwan Semiconductor within the accelerated computing subject: NVIDIA’s cuLitho computing lithography platform is now in manufacturing at Taiwan Semiconductor, considerably rising the velocity of the chip manufacturing cornerstone step of computing lithography and decreasing vitality consumption.

NVIDIA acknowledged that cuLitho is introducing accelerated computing into the sphere of computing lithography. The manufacturing deployment of cuLitho permits Taiwan Semiconductor to speed up the event of next-generation chip applied sciences, as the present manufacturing processes are approaching the bodily limits. Making use of cuLitho in manufacturing at Taiwan Semiconductor can improve the velocity of producing next-generation superior semiconductor chips and break by bodily limitations.

Computing lithography is a key step in pc chip manufacturing. ASML defines it as utilizing pc modeling, simulation, knowledge evaluation, and so on., to foretell, appropriate, optimize, and validate the imaging efficiency of lithography processes beneath a collection of patterns, processes, and system circumstances. It entails complicated calculations resembling electromagnetic physics, photochemistry, computational geometry, iterative optimization, and distributed computing, making it probably the most computationally intensive workload in your entire semiconductor design and manufacturing course of. Wafer foundries sometimes arrange massive knowledge facilities particularly for these calculations. With the dimensions of superior chips getting smaller, all the way down to 3nm and under, the necessity for exact computing lithography is rising, and the time required for computing lithography can also be rising. With out extra highly effective computing lithography, it’s troublesome to realize complicated masks designs. Computing lithography has all the time been a bottleneck in bringing new know-how nodes and pc architectures to the market.

Eventually 12 months’s NVIDIA GTC developer convention, NVIDIA launched the cuLitho computing lithography know-how software program library constructed on GPUs, which is taken into account a game-changing software program within the subject of computing lithography. The core of cuLitho is a set of parallel algorithms invented by NVIDIA scientists, the place all elements of the lithography course of can run in parallel. What used to require 40,000 CPU methods to finish can now be achieved with simply 500 NVIDIA DGX H100 methods. Wafer fabs utilizing cuLitho can improve their day by day masks manufacturing by 3-5 instances, whereas decreasing energy consumption by 9 instances in comparison with present configurations.

This Tuesday, NVIDIA reiterated the achievements disclosed at this 12 months’s GTC convention. Superior foundries spend a whole lot of billions of hours of CPU computing time on computing lithography every year, with a single chip masks set doubtlessly requiring 30 million hours or extra of CPU computing time, necessitating massive knowledge facilities for foundries. By accelerating computing, 350 units of NVIDIA H100 Tensor Core GPU methods can now exchange 40,000 CPU methods, rushing up manufacturing, whereas decreasing prices, area necessities, and energy consumption.

TSMC CEO C.C. Wei acknowledged at this 12 months’s NVIDIA GTC convention that by integrating GPU accelerated computing into TSMC’s workflow in collaboration with NVIDIA, TSMC has considerably improved efficiency, elevated throughput, shortened cycle instances, and decreased energy consumption
NVIDIA revealed at this 12 months’s GTC convention that since its launch final 12 months, cuLitho has introduced new alternatives for Taiwan Semiconductor’s revolutionary patterning know-how. Assessments carried out on the shared workflow with cuLitho confirmed that NVIDIA and Taiwan Semiconductor have respectively elevated the velocity of curvilinear course of and conventional Manhattan course of by 45 instances and almost 60 instances. The distinction between these two processes lies in the truth that the sunshine masks form within the curvilinear course of is curved, whereas the sunshine masks form within the Manhattan course of is restricted to horizontal or vertical.

On Tuesday this week, NVIDIA additionally talked about its developed generative AI software algorithm. It has been confirmed that this algorithm enhances the worth of the cuLitho platform. Constructing on the accelerated course of velocity of cuLitho, the brand new generative AI workflow has additional doubled the velocity. Making use of generative AI can create almost excellent inverse mild masks or reverse options, fixing the diffraction drawback of sunshine in computational lithography, after which deriving the ultimate mild masks by conventional rigorous bodily strategies, thereby rushing up your entire Optical Proximity Correction (OPC) course of by two instances.

OPC has been utilized in semiconductor lithography for thirty years. NVIDIA acknowledged that previously thirty years, few applied sciences like accelerated computing and AI have introduced such fast transformation to OPC. These applied sciences make bodily simulations extra correct and obtain mathematical strategies that have been as soon as resource-intensive.

Many modifications in wafer fabrication processes at the moment require modifications to OPC, rising computational workload and creating bottlenecks within the growth cycle of wafer fabs. The numerous velocity improve in computational lithography accelerates the creation of every masks in wafer fabs, thereby shortening the full cycle of creating new know-how nodes. Extra importantly, it permits new calculations that have been beforehand not possible.

NVIDIA cited an instance that twenty years in the past, reverse lithography know-how was proposed in scientific literature, however resulting from lengthy computation instances, correct calculations on a full-chip scale have been largely unachievable. With cuLitho, that is now not the case. Main foundries will use it to reinforce reverse and curvilinear options, which can assist create the subsequent era of highly effective semiconductors