mpw-008 / MM2 Metal 2

the MM2 shapes of all of mpw-008

40 of 40 dies rendered.

555,342,603 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (552.0 MB in total, largest 22.7 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-008_MM2_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-008_MM2_reticle.png
the stitched reticle, full resolution
261.6 MB 00cb8cec3689999d27039c554b1a5b96b9d4d6cd8ff6ec53a3083d0787c214c2856f470b 46aac4220b63217ea11d73b0821af6b0262b3058fcfd18f2a4b79dc6b591b068
mpw-008_MM2_reticle_overview.png
the same image scaled to fit 4096 px
6.0 MB 86b06630bd07df5d17c176937d0a0c54c11b9376e4cb6e59a0d1b040cde21f265fcdc0d8 8c7ef03b211192c56a1b57fb89c27f1af8ee8211189ff3d8b0cdffae70397d03
mpw-008_MM2_tiles.json
where every tile belongs, and its checksum
13.5 kB fc18df5381b16a1bc4aed0ec15356e73dfcf3fde63c101e41c22d2b0286588885bdce003 911c3f46fc8de315dda59f260e3fa7f9536a5ef91497ba31e56baf52918a2f09
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 wishbone_pio 7576×107760, 0 17.2 MB71f27fe2ed9db1748fb5de990c0abf4237833bad0b2d0d95ed966efa4daf692e
A2 iit_indore_mm 7576×107767576, 0 9.7 MBa2c6b1ea85dd5747e00596ee1861a387f84db3b47b4e0e6f9a39aba2ea7d169b
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 12.7 MB73c3439f0dde9f1d450d63bb7c1bf45defe4fc9412a9430afdc32015fe42d5cc
A4 riscduino-dcore_d4_ 7576×1077622728, 0 18.8 MB156dbeec13fb140738de3bab197bb50da2851fe7e773b5374894fe62cfddd020
A5 riscduino-qcore_q3_ 7576×1077630304, 0 19.0 MB575f788bd59f64356c79f39aceb43d2495626bd92973cc6bb6efbc165f7a6dd1
A6 caravel_mpw8 7576×1077637880, 0 12.8 MBa64b49eaf4bc62ca0b6e2b278d7682c7433f6e93f2435c03d749871c06f5f018
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 11.3 MB73f185040853989d652d6c6052c22cf88e928afc28adc329891f4e8cfd80410c
B2 sramtestvehicleg4 7576×107767576, 10776 8.2 MB1deab6a8e2e58187cbea7bbab339fa6038f1ba52b279ded79c483e6fe758553a
B3 reram_crossbar_rerun 7576×1077615152, 10776 11.0 MB377b6dd3c24ef9d2928a65e0d7678c3555461869b6a957168f9844cb6eb3c07d
B4 tinytapeout_skullart 7576×1077622728, 10776 19.2 MB2d28b8874d84a382752b2c1661e1c60fb905e5a046ccee4babb927c41b581c57
B5 soomrv__mpw-8_ 7576×1077630304, 10776 21.9 MB32a58e28d718c44b70009b50123eb263f6ab9ca7cfd30a56ca4f506c30ee0532
B6 clock_divide_selector 7576×1077637880, 10776 10.7 MBeeaaf2825293d8a4c6980bb0917e0875158da044ed881a9b634bf36e485bd748
B7 reram_module_mpw8 7976×1077645456, 10776 8.4 MB3d0ad6ad283b942e3fe8f2501cb75d36e7c291c8c9f517b35a27e46677670166
C1 shapingthewaves_8 7576×107760, 21552 18.3 MB9320a05d0e78f2b53a70ca43b7ebc41195412f83de0eea6a77c6bf18c53f9399
C2 leosoc 7576×107767576, 21552 16.1 MB4a843cdb54291d053c6c0715fa8b56f6fcc55e9035a5120705ab40389de7a703
C3 decrypt_aes128 7576×1077615152, 21552 15.8 MBace0914c00e9a2a460a5750c657abe5493f1dbf45cf70acaf0658bca521cb331
C4 approximate_adder_axha 7576×1077622728, 21552 15.6 MB30dde604f1838ddf8338320cce0269ddeebdefba6e72b5e43ac94e413d8e346f
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 14.8 MBa6626b346165ba8628867102806dad234535e8c22780c3b88152bfaa1d287a36
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 8.3 MB575fb684169bca7cf493f28333f3400f7c47871f2a06ccb0039ebb5527aa50e4
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 22.7 MBbe3e4b2464e1b61d7a92b75651a4f065fe9e43b6028f1640601d19b8f7e853c1
D2 hyperram_controller_for_sky130 7576×107767576, 32328 11.7 MB9aa0e37de31351c4325f5d6de051907fa8c3e14f5f7abf0f0a47e83e446a0bd0
D3 hardened_alu 7576×1077615152, 32328 13.9 MB08d5990324324be6d70647747fd46740da6441ccf6cc590a61f027e48c71edbf
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 17.1 MBf05dc6e604748f29d0365e6dd34876c65583ad53555990fe9b1225aa6c76ccdf
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 8.3 MB7eff26b9b2a92c4f3c6a401aefdedb61a32484a3a6ffc786e74fa0f06698755b
D6 gf180_vs_sky130 7576×1077637880, 32328 15.2 MB182f27ba0bf6115092745f652ef286621474e47397e7f5d237b3499ab478f4f2
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 15.7 MB3be172881bd7db2eb6606c1970f2e100c5f227498251787401412dca0c1b6828
E1 iiitb_sd_mpw8 7576×107760, 43104 15.6 MB0ce4797c411762ded32eeab53f77d17b9f9b4e1b41a0a8ffa474a16b0464a0c7
E2 iiitb_sfifo 7576×107767576, 43104 12.7 MBcb665a1ea5e87e04344c6f478af2774341ef5158c1ed7e561d1918fedd01c3e9
E3 iiitb_alu 7576×1077615152, 43104 15.6 MBb91da55fc7f33164ee0cbdd20c8a44bf57a5630abb9d20424aab559796bb3c11
E4 ppcpu2 7576×1077622728, 43104 18.9 MB40680f2dc71b2d9115933363f9c6b605e30782c55f224fa64804d9e29a89f64b
E5 mega4_reram 7576×1077630304, 43104 9.1 MBdf98ae1fddddfa4e774d5c5901174a01c837f6b687a9e7a49f50507aa8d84d1a
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 10.7 MBef375dfbeb9ce5cab2ba6a669ca365bc968d4cf7c6bed2aef54d66ae418e62ba
E7 sky130cla 7976×1077645456, 43104 9.2 MBefb23fb51d02d4545671a93887c470973010d50b05266940cae28f4b5128870f
F1 uetrv_esoc_v2 7576×111760, 53880 13.8 MB523996424b602b78f6b87a88cffc778eacf296455d2ad50363d169ee75e79e3c
F2 sequencedetection1010 7576×111767576, 53880 15.6 MB230605ee2dc3169680b7d0cafa4aea34f26f31696f21a145733c963258543a4a
F3 testasynctrimux-mpw8 7576×1117615152, 53880 10.7 MBfc3723cbf1535f5fde8170610b4d3aa766635a5285dc33e29d97fe6557f0715a
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 9.6 MB90e4cd3da5ba5f734fc6997b06deccfa8afc0f3756bb55f3c8ede94927311df1
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 10.1 MBf1d4ce38af76b78cf26161bc4e5c66e46fffbdb6712798fbb1c1dba3c249e7c6
F6 arbiter_puf 7576×1117637880, 53880 9.2 MB06f3a05883b4f9a08e6e1508d6b9feaa415730b4c121ddfc1d7e103542d073d8
F7 unigate-sky 7976×1117645456, 53880 16.3 MB0854132aae4e0e18b31b4f691538fa40497308206c4b31150b6e8f82b1431d68

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About MM2 on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask MM2

Mask layer
MM2 — Metal 2
Process step
124 — Metal mask2
Drawn layer
69:20
Layer on the mask-layer GDS
41:0
Fill layer
41:28
Also
Metal 2
Used in SKY130
yes

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.