Advanced low-complexity compression for maskless lithography data

A direct-write maskless lithography system using 25nm for 50nm feature sizes requires data rates of about 10 Tb/s to maintain a throughput of one wafer per minute per layer achieved by today’s optical lithography systems. In a previous paper, we presented an architecture that achieves this data rate contingent on 25 to 1 compression of lithography data, and on implementation of a real-time decompressor fabricated on the same chip as a massively parallel array of lithography writers for 50 nm feature sizes. A number of compression techniques, including JBIG, ZIP, the novel 2DLZ, and BZIP2 were demonstrated to achieve sufficiently high compression ratios on lithography data to make the architecture feasible, although no single technique could achieve this for all test layouts. In this paper we present a novel lossless compression algorithm called Context Copy Combinatorial Code (C4) specifically tailored for lithography data. It successfully combines the advantages of context-based modeling in JBIG and copying in ZIP to achieve higher compression ratios across all test layouts. As part of C4, we have developed a low-complexity binary entropy coding technique called combinatorial coding which is simultaneously as efficient as arithmetic coding and as fast as Huffman coding. Compression results show C4 outperforms JBIG, ZIP, BZIP2, and 2D-LZ, and achieves lossless compression ratios greater than 22 for binary layout image data, and greater than 14 for grey-pixel image data. The tradeoff between decoder buffer size, which directly affects implementation complexity and compression ratio is examined. For the same buffer size, C4 achieves higher compression than LZ77, ZIP, and BZIP2.