mpw-008 / MM3 Metal 3-PLM

the MM3 shapes of all of mpw-008

40 of 40 dies rendered.

232,648,596 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 (200.7 MB in total, largest 10.0 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_MM3_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_MM3_reticle.png
the stitched reticle, full resolution
90.0 MB bfd68e994d3a2a0f808a1e196c6ff0d293fd6f308506ea1392588405474c5aa882a7b9f9 75d9db663c03f97448137a4d4bd85221361d713226fdd34a2c021177e23d6e78
mpw-008_MM3_reticle_overview.png
the same image scaled to fit 4096 px
5.2 MB 6d2366643d319b922a14986ee2d9a4fabc9db42cd92f6a9fb14c541bddd8f39818f993df e0786a31d8e574c96ea2407ad9b3c3f9301e6730bc1fd73c16ac85173f370286
mpw-008_MM3_tiles.json
where every tile belongs, and its checksum
13.5 kB 2d5c0730a4e147c96e43a4e3de5eaacfd2324930f36154a4abe761d17cc35f016bd15879 d124baa61500586828ab187c6d7bd40b1fcd9adad1dd8881c69e61430be0d813
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 4.7 MB11e005c4a520869b85ebd66f8f87fcb50ea7ff24702218c5fc1cd145c42b94cb
A2 iit_indore_mm 7576×107767576, 0 3.9 MB39b32c594bc215b1135a1847f3c8f9fa22a83d8e891ed9688621157afde775f3
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 3.9 MB9532c87bf8a189f79b12552b132d6182600f7398a7fcd3b59a347b7c31fe2ce6
A4 riscduino-dcore_d4_ 7576×1077622728, 0 7.1 MB4f11d3cf791665c75a48c3718c0b022556b7b2f27214ea75c1aa0969f8ed944a
A5 riscduino-qcore_q3_ 7576×1077630304, 0 7.4 MB653ae4ce5bec16aa203f0956db94926167584be3bb1ec659abfb2ac181d4d47a
A6 caravel_mpw8 7576×1077637880, 0 4.0 MB41c11cac2eda90ebda43ef73c98b65878db7c8d2cc8817ab4dc78a3a13189405
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 6.4 MB013ccce0e7a92d8267d09245a53b6dc2bec1105232d4f000d607063e97b3d8a1
B2 sramtestvehicleg4 7576×107767576, 10776 3.8 MB57e9f7db07e4cf23b3a6f193244fde1f5512f701492ce4addee60b63cb18cd52
B3 reram_crossbar_rerun 7576×1077615152, 10776 6.0 MBa5f21bcf0a3ec452aee728153275198959669fde5fb507993e38238c9375d62b
B4 tinytapeout_skullart 7576×1077622728, 10776 10.0 MBa7e66b64d156c2950e0a3777ff04d1d767deac5d8a87432150a89671f22cdaa3
B5 soomrv__mpw-8_ 7576×1077630304, 10776 7.4 MB07f8338db47909c22c6ab335d288a64b8d436fb85972ba8b2f5e88c71b4e1d70
B6 clock_divide_selector 7576×1077637880, 10776 3.8 MB87e28a60816b96fad9ef25a14003ee5c3565fe13d0c61c0a61180014065839fa
B7 reram_module_mpw8 7976×1077645456, 10776 4.6 MBb187299e65a5268c6f12b9d0fb8da3acea5bf4ddf3d38c181b68997fe6fad812
C1 shapingthewaves_8 7576×107760, 21552 6.2 MB77d7883efe769b17708b7b1e682ecb320154ead5db9934fc7f244449ede8e1ca
C2 leosoc 7576×107767576, 21552 6.7 MBc6cabfab16a646f05020e42d3a94c941c58c264f624053a483d7f84864c4376c
C3 decrypt_aes128 7576×1077615152, 21552 5.1 MB8a1b31b55ae5b0816d2a67973c3a314855769b49453df58f4e11970027222818
C4 approximate_adder_axha 7576×1077622728, 21552 4.0 MB17d61a82e8ad493b62089e8e2f2ffe5e1487d30bd4a7cba234b1d44e8ca52d7b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 6.0 MB19f8d5a6b275f00739e21420442f7bae806ea9c45c3d09905ebc8aa08596b3eb
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 4.8 MB98311f4f21785e4a714e2dadd4c5211f230219814bf7d4618c0f16ab6a179872
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 7.2 MB287dbe684572bd485c7e32a27e94f787d877524f9a304b1bbdcfca501e4ede7a
D2 hyperram_controller_for_sky130 7576×107767576, 32328 3.9 MBbf9acb986e1cafc123f3c9f958d008906f21af9f7293761f3e9d4d4b3cb02203
D3 hardened_alu 7576×1077615152, 32328 4.3 MBdafb0984983ade569211f0fc2e4b45c30f73e9ddfc19aa1d1bd0c70c73b3cf50
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 6.8 MB48a360b046bfda4a3b77d820bd926e2dccf67f91e2c10e3a2d3996c662d697d9
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 3.6 MB2af1cf104f5712f008fac3b191a5318b09bc4b1749b506ab172dc6470b0923ce
D6 gf180_vs_sky130 7576×1077637880, 32328 4.9 MBfe6475b682e60704a10f88958650a1e0a92dff5a98abff76dd622be6debcf895
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 4.0 MB394df2e7afcbf685e91f0162f7912759966eaf3fdc3adfed78a295501cafafa4
E1 iiitb_sd_mpw8 7576×107760, 43104 4.0 MBd97f5313327c0805ffdc9fcb2c274e59b823749fe34e71e1a5cb794582af6b71
E2 iiitb_sfifo 7576×107767576, 43104 3.9 MB27b0b4d169d83ae34b59897d3b112599c26e3e60f7d64b9e8b037b6c89b048b2
E3 iiitb_alu 7576×1077615152, 43104 4.0 MB06312496985adfa9e5d1905926980c56ade758a994e84ebc53ffeeadac573e87
E4 ppcpu2 7576×1077622728, 43104 5.9 MB27e0b725764005c5df36727655946640f2a3d2bc5291e10d81bf65b6acc2978c
E5 mega4_reram 7576×1077630304, 43104 4.2 MBb81541a416e0cfedefab644ce94a5076c441b5366acb393eec126ab86b3c8faa
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 3.8 MBb29c8170fe9d54d480cf5230f50566bd1a32323be503a1b460067d9d190403c3
E7 sky130cla 7976×1077645456, 43104 3.9 MBfec21f5501d917c8ade73305a843dd1957822ebff8082bcdf2ad8149b90f2ed4
F1 uetrv_esoc_v2 7576×111760, 53880 6.5 MBdb5e34394736c06a17772540c24e91f20e0dd393b14319ab1c986a812973fc21
F2 sequencedetection1010 7576×111767576, 53880 4.0 MB7e29a598855a5e692321f79c296af3947b233958dc666bb12c4c5002aa7daa8b
F3 testasynctrimux-mpw8 7576×1117615152, 53880 3.8 MB50e6c664f3512f81ac213a517bf531a086476d23c49b8292855245f70c02ca48
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 3.8 MB4622a4016f24c5ab2f3902c9396ea1597383cfaff498e22d1bdd2330621d66ff
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 3.9 MB431df67d5e746e026957a96a7d8ca218c82fd06cef6ce06b7b949733ec9e1571
F6 arbiter_puf 7576×1117637880, 53880 3.8 MB9c158a92523711fea2a0b31ea594062f9371d45a4ee5e25b5856a8abb63f6fe0
F7 unigate-sky 7976×1117645456, 53880 4.9 MBf6e88c037d3506ff5e63990f1a8da5d8ff2fa25a622f3aff4005a57bac26b78d

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 MM3 on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask MM3

Mask layer
MM3 — Metal 3-PLM
Process step
139 — Metal3 mask
Drawn layer
70:20
Layer on the mask-layer GDS
34:0
Fill layer
34:28
Variants
Metal 3-TLM, Metal 3-S8TM, Metal 3-PLM
Also
Metal 3
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.