mpw-008 / MM1 Metal 1

the MM1 shapes of all of mpw-008

40 of 40 dies rendered.

697,813,194 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 (304.2 MB in total, largest 15.9 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_MM1_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_MM1_reticle.png
the stitched reticle, full resolution
156.9 MB b82d95fbe43086d598d51bbf97608708d9c0361cea3f6aee551f05b9e767135d3c121293 d6cc06cc8f747fa77bf21f7dd63d38f11e9465a54a8144ea585d4390d362c574
mpw-008_MM1_reticle_overview.png
the same image scaled to fit 4096 px
5.2 MB 03147601aadd29787ffa2d989fb09269ac584f667bdf4999d9ae4f13bbd4fc78676d15ce 6d02145420871b3b20e200f9949d0143488b5b65edb8a4e4d216fa2de09a4bf6
mpw-008_MM1_tiles.json
where every tile belongs, and its checksum
13.5 kB c9a0ac164cac2c92745a6b725096fa23c679ebb9809ad49798e9bfed7531513e158513a2 59802c40d8b04e6493e92311b9f300b9a23f7547f6e08758d89b5aa45b3a6db7
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 7.4 MB4c00b96b4e8a222954ead44eb547e3767df40200d42040ada8ee2a3489d42ae6
A2 iit_indore_mm 7576×107767576, 0 5.8 MB3d6d31c7e997d245a8d7141fa154879f90d85d502d59a69b6500f96cd76e6bb7
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 5.8 MB241a6fea12063629aedbe034ba070da39d4c1ebf0a71f09620cfc22262865a5d
A4 riscduino-dcore_d4_ 7576×1077622728, 0 12.4 MBccffbe9677c3b19d68ecf8fe8ea589434393715effa14faef0f1b636b7b6c6d4
A5 riscduino-qcore_q3_ 7576×1077630304, 0 12.8 MB7efe9dcad2d5563711689dec922f6565660f56f339ec82a1c7a67bc8297b3d78
A6 caravel_mpw8 7576×1077637880, 0 6.0 MBa62efdb4d7817f878b7e4f1be2fd2a8cf75904ceac8e9a9d555cd78d2cc827dc
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 8.5 MBc242d814c5330809d6a7374456de71114bae56b59f11c4e0ccca562098e39203
B2 sramtestvehicleg4 7576×107767576, 10776 5.9 MB58e3f980626cf3a15b4421665c5044543f6c013c0f8e7626e275fb2a40379379
B3 reram_crossbar_rerun 7576×1077615152, 10776 7.1 MBd10b392a1c604b82c0f97099cb3a2979bcbd11c852c99f3bb6a7c21eb480f23b
B4 tinytapeout_skullart 7576×1077622728, 10776 9.5 MB40beda682fa61ca97414757f10d278728d6a5e5b70469687964a816bebbef389
B5 soomrv__mpw-8_ 7576×1077630304, 10776 15.9 MBa7ecdf84f5f878e5405440eb14043bad68333af2c2179df0dfd0f0920cea00f6
B6 clock_divide_selector 7576×1077637880, 10776 5.7 MBa947f9781ee9a24ea7b445a20b72530c7a652372278097c5eb297a22563212e1
B7 reram_module_mpw8 7976×1077645456, 10776 5.7 MBe23abeac45b1708598adcf9c822a35b13198c1a49b0a893521c15f5d6e13619e
C1 shapingthewaves_8 7576×107760, 21552 10.9 MB7084df13ce54714ff0fa0bf9ecc9d98f58c52e72910884e4c8b6591df1888c0d
C2 leosoc 7576×107767576, 21552 8.2 MBc2ff3d61bd2db2908f77124523594c3bac024c31103b56c975c7a4a0ffb6bae9
C3 decrypt_aes128 7576×1077615152, 21552 7.9 MB40562281cb4d750758a66638ebeeecfb99c1632dc6ec59d8603fada4e1eab26c
C4 approximate_adder_axha 7576×1077622728, 21552 5.9 MB846b39bb06628adcefea6fbabc9650fc84fa43c0c14a3e354a4598d7501c1b4b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 9.0 MBb7d7642aaacad4e77e9387dce804403d470f1ce2205e29186848b35720a8af32
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 5.7 MB4ee3540390ea9a27097ee46637a1242823e35ce26be9e7bb08a1f229108da18d
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 15.0 MB64185828b11b2f3aee050ea194ecad9275262ad3e9e2872a16f149b3b7dea34c
D2 hyperram_controller_for_sky130 7576×107767576, 32328 5.9 MB2994c60dd98601e6c99596dccaf40e853902fd86300b363837b12c61354df6c9
D3 hardened_alu 7576×1077615152, 32328 6.4 MB287503b67dfc81edf9aecd76b6182861172537e1787af52f549b15735c3da5f2
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 9.6 MBa93fd5f68b44e17a34e6ba391cc7ca00ea37373ffc221df63e8640f8a875ade2
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 5.6 MB9901644ce4c090880e05141407cf5bb715072d55938a4071b9cda9604ed57698
D6 gf180_vs_sky130 7576×1077637880, 32328 7.7 MB566be32b0a3d7a37fee603e65038c8018d7fb502bfdc569c42aa44a41ea28e8e
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 5.9 MBcad7d1e29b918cefcd8a687463c4f86d31fe8aacbde0a8b41d3881c16a20971e
E1 iiitb_sd_mpw8 7576×107760, 43104 6.0 MBac407c6c63774fa92cccd3f6faab7535eb5443a96f33c3ad6bf562bc148ef999
E2 iiitb_sfifo 7576×107767576, 43104 5.9 MB88ba40e1f848f8df0673b2bbfa9e2a0b13f072e9109b60ae53a3402702e41182
E3 iiitb_alu 7576×1077615152, 43104 6.0 MBb38ee3b6f6044fab4d8673f914b4aabbae031ada3ed5e475ac52a34fd4011230
E4 ppcpu2 7576×1077622728, 43104 11.8 MBed8cb928bf022c19f55c91eb885a8e1377ef0da8cc061f08e5e727a9137e4e25
E5 mega4_reram 7576×1077630304, 43104 6.7 MB9f17e27344d5ad62d0944c80195718ec283da2210f4d05bf96c530b058e0747c
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 5.8 MBed44e477ea755f486cf9447715efed016673b63d1458e4ad2e6c8db360409b34
E7 sky130cla 7976×1077645456, 43104 5.7 MB52d58175009dcab6b3645a261fb1db75f0d1178a3f93b53898f699807e12b23e
F1 uetrv_esoc_v2 7576×111760, 53880 7.4 MBf0ba769f1bc6e70a76dc77fc35c9ba91ff46cb7d61dc8b7ac05e717f1eb82b4e
F2 sequencedetection1010 7576×111767576, 53880 6.0 MB9432f3830a5ccfb73b95cf34ef8b5602727d9b5e93dedcc0eac006bba60dd3a5
F3 testasynctrimux-mpw8 7576×1117615152, 53880 5.8 MBae23db27fb08d88a472521abb0df265c4da7e832e64d4423c5ded12ae3ad1e1c
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 5.8 MB83ee22fc0c00f81d0e6965c251ce08ef4aecc8de7ea79f3315185beaf707ec36
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 5.7 MB1d617c0cbf115e153515d5255c0f450e0d06f63f322fd31c4cb021f38b62b8d2
F6 arbiter_puf 7576×1117637880, 53880 5.7 MB1918873ded42856553939a35bf457c17c99cee8b2aa3eaa4f7a536a71888ca04
F7 unigate-sky 7976×1117645456, 53880 7.5 MBc9a278a597a3322a3a08a09608cbc222d86ff810cc39ab9633f323c3cf552c4e

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 MM1 on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask MM1

Mask layer
MM1 — Metal 1
Process step
113 — Metal1 mask
Drawn layer
68:20
Layer on the mask-layer GDS
36:0
Fill layer
36:28
Also
Metal 1
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.