mpw-008 / MM4 Metal 4

the MM4 shapes of all of mpw-008

40 of 40 dies rendered.

234,012,550 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 (391.3 MB in total, largest 12.3 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_MM4_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_MM4_reticle.png
the stitched reticle, full resolution
163.7 MB 514247c5e4017bc819ffd5b675a25603eaecf4bd83637b7a81871c2444201cd85ffca89a 6f05fc2009bdeb196f9cb753eaabf8c2efcc4c58694e0718ee3b664ec6d09db5
mpw-008_MM4_reticle_overview.png
the same image scaled to fit 4096 px
6.2 MB a1589debc868efca6fc5259f944b0705eac0201af9e73120e8a8a2dfe1d6301692678b33 c409efd057c7d998a889e66d68a86d3f7bfea9507476da3c29670ef41d88bacf
mpw-008_MM4_tiles.json
where every tile belongs, and its checksum
13.5 kB b343cb406adbeec98753b46059a7992dc3f87f746387f887fcbe9a75dd0b0ac0f9b12a20 b14e3a63355c85d9a1af31ad81276f496a56ab4f87563d4ce2a78edbff2dc028
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 11.5 MB7b2db00bd728e1bfc4a3840614f1082affa8cdfe67a6f303097efad845af8afc
A2 iit_indore_mm 7576×107767576, 0 10.8 MB72fc0223b7118711b4197ba7428d8ee0c0ff983e176789ede0a6c2429be5f20c
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 10.9 MB0eb69f3282c413e3d2416534fae1bb6065eb601fc238f4e989fddd2f774eefe0
A4 riscduino-dcore_d4_ 7576×1077622728, 0 10.3 MB47db37a04bbf9f2b038a5240993051e586651a2ca4174bf04c2a8738286956f3
A5 riscduino-qcore_q3_ 7576×1077630304, 0 10.9 MB4f0b306e23af8467e0e5b67188488096812611ca980a9f86c866b1a541c76177
A6 caravel_mpw8 7576×1077637880, 0 10.9 MB3b0fe03e1cd41ce137f86f8a4fbc0d4a9a6d1b46120e9ff0063809a3d5418155
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 6.4 MB89416d03d39f776db5e1eb01acb0e8fbb405fb2c8e39e97eca751c54c471e41d
B2 sramtestvehicleg4 7576×107767576, 10776 3.6 MBf6d448d10b331d0612981694b9dd434860c2be4679b6190c5101440a1e8cb80f
B3 reram_crossbar_rerun 7576×1077615152, 10776 6.8 MB29e2065bea15c2411dd1ee5376899462592b9bac832f0ef909aaa374480cb6b6
B4 tinytapeout_skullart 7576×1077622728, 10776 9.4 MB6b7d6d89af03201b9d399be1cdd17db2965ee57b01a76fad4601c503f412c6f6
B5 soomrv__mpw-8_ 7576×1077630304, 10776 12.3 MBa1a0e9a4d85a753b9440f94e42e8ff7f94cb8747bf7a0128566978a389676be9
B6 clock_divide_selector 7576×1077637880, 10776 10.8 MBabd314076317b1e799165bc107c8728b0a8259dc86ea0f912dce9026c1078d97
B7 reram_module_mpw8 7976×1077645456, 10776 4.4 MB42282e5f26e4a356f4d6f166460a2bff438acc5e52148a7ac33075021894f5f9
C1 shapingthewaves_8 7576×107760, 21552 11.0 MBc5b8563386bc00bcd0ee231f70472a9d0cd6fc0d9b45a4fde4b75665903302e0
C2 leosoc 7576×107767576, 21552 10.6 MBba93502f2d7c906705b10e805541a64f3f4d9941069135c35b26c6eb72d16ce8
C3 decrypt_aes128 7576×1077615152, 21552 11.2 MB9109cf481ad8e287070f12b5a4b8e6a0bcc141a9a87ecfc8ebfd5d7a6a8b4950
C4 approximate_adder_axha 7576×1077622728, 21552 11.0 MBa34d363164a54b6fb8838744df67bf1da042cf895dcb1ce5e535ae35099595ed
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 8.7 MBc32fdef15d998ecb6b5dba5fdd3661424c244492e3f12aff330d82265fc2f717
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 3.9 MBd53f9476746dd46bbc9ddd3bba63fb8e30dfb12656fd66863ee2af94b3f9a528
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 11.3 MBd80b684cda1713168fe2496dee2351a42e7994536510dbcfd3e56c8d3c47c353
D2 hyperram_controller_for_sky130 7576×107767576, 32328 10.9 MB9cc4c514ee06d400e0fc695841b8876f3d953029e6478912556475098b12c535
D3 hardened_alu 7576×1077615152, 32328 11.5 MBcbecd84685ac66cff72c0ccf11118b31514cc6770ac1beedb6a46b9842992048
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 9.0 MBba86e0abca011f691e5bb5da4841bd4e5dbd24f244ffda2610b104d97fef3685
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 3.6 MB77cb46c3f8e9c9392c85015a2f70e503b79d7c9637c0053944c830f93506d57e
D6 gf180_vs_sky130 7576×1077637880, 32328 11.3 MB0de2b68e3a303e18b65a069ca3f0cba90f2ad6c3b17cdd329b4013469f6872cc
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 11.0 MBd9d70b2665a110c845d4bf70c0cfac8c0dd54a1f3e1f840dc6c3254bba1bbee5
E1 iiitb_sd_mpw8 7576×107760, 43104 11.0 MBc9f42f402a030cf34944ddd013c842cd092d591d40defcb8f61e5e78be48916d
E2 iiitb_sfifo 7576×107767576, 43104 10.9 MB1b484089fb29736183c0583d9a6594a25e4159043eec36c3b76df8f75e2e2ae0
E3 iiitb_alu 7576×1077615152, 43104 11.0 MBcdf6adf90cfb4005f7debccf0ace206ad72b1299cf49f59421da61166b667234
E4 ppcpu2 7576×1077622728, 43104 11.4 MBd9d34976757524748209ceb80bdcf7077fbdf06309d0a0b925d5ea0c0c887edd
E5 mega4_reram 7576×1077630304, 43104 4.1 MBa1430df14b93f7c057fd37a0a61c163c33679ca6d910facb679aa6b43145d8e2
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 10.9 MB093af2bbb32c15dd5e8b5fe50458a2f7519d6648f2a6a9c73e8b3f5f72fb9201
E7 sky130cla 7976×1077645456, 43104 10.9 MB4f1961db5ca2b30b9bae719015e853a9bb0e5d05bb93d4dc147e1fcc61231dcc
F1 uetrv_esoc_v2 7576×111760, 53880 10.8 MB21337500f5531b587264b019154dfc58959b78cde8d342c99077b481d8de1c34
F2 sequencedetection1010 7576×111767576, 53880 11.0 MB1dd0460b34cc538cd91367ecfa84489aba3a19b8eb0398abbeaa56f7fc279938
F3 testasynctrimux-mpw8 7576×1117615152, 53880 10.8 MB12707233d6bca823d6ffcc7d9ffc58a83da8992208a41fcc0b33035a33690ac0
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 10.8 MB4cb546d4e2ec862cadd0d64b81ccce54d9ab22536f373bb313680209cdc7e983
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 10.9 MB9813e31d12df416ba334f174bb1b0ed7dbf9a01021fbe94257961572bbdd9f2e
F6 arbiter_puf 7576×1117637880, 53880 10.9 MB704fe5fee46ca9f3ebaca5dd67b21041ba7fafeb63741bd0c377d14b9953ddd0
F7 unigate-sky 7976×1117645456, 53880 11.8 MB16cb7baa9662a3a77a6a667621230207cc9b810a9f3f7d5e76f7b621436137ba

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 MM4 on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask MM4

Mask layer
MM4 — Metal 4
Process step
154 — Metal mask4
Drawn layer
71:20
Layer on the mask-layer GDS
51:0
Fill layer
51:28
Also
Metal 4
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.