KEYWORDS: Computer programming, Electron beam direct write lithography, Raster graphics, Electron beam lithography, Image compression, Logic, Detection and tracking algorithms, Semiconducting wafers, Data compression, Data processing
Data throughput is a critical metric in a multiple electron-beam direct-write (MEBDW) system so that heavy-duty data processing equipment is required. The main challenge is about how to achieve high performance with cost-effective techniques. We propose a high compression rate algorithm for efficient data transfer and high speed decompression hardware to raise data throughput of the system. The hardware decoder uses pipeline architecture, a run-length encoding first-in-first-out queue, and parallel dispatch logic to increase the throughput. The decoder is evaluated on field-programmable gate array and simulated with layout images that are compressed using the proposed compression software. The results demonstrate 18.2% better compression rate and 254.8% better throughput than the previous work with similar hardware cost. Because no static random-access memory is used in the design, the channel numbers of the system can be easily scaled up, which makes it possible for the next-generation MEBDW system to achieve higher wafer per hour targets.
As one of the critical stages of a very large scale integration fabrication process, postexposure bake (PEB) plays a crucial role in determining the final three-dimensional (3-D) profiles and lessening the standing wave effects. However, the full 3-D chemically amplified resist simulation is not widely adopted during the postlayout optimization due to the long run-time and huge memory usage. An efficient simulation method is proposed to simulate the PEB while considering standing wave effects and resolution enhancement techniques, such as source mask optimization and subresolution assist features based on the Sylvester equation and Abbe-principal component analysis method. Simulation results show that our algorithm is 20× faster than the conventional Gaussian convolution method.
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