mpw-007 / MM1 Metal 1

the MM1 shapes of all of mpw-007

40 of 40 dies rendered.

689,690,573 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 (336.7 MB in total, largest 16.2 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-007_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-007_MM1_reticle.png
the stitched reticle, full resolution
191.7 MB c348850dc721d850868c759b22fb9c9d278347b7928152d0ac321d1d9393329be5ba13eb 704dc69d60f7f3a7f08eff2d5c0e2429d71b73f9febb62817187fab9e27c2e38
mpw-007_MM1_reticle_overview.png
the same image scaled to fit 4096 px
5.4 MB f307476c7b13dff4e105d8b49f41b369c620002e4d57ac1d22a1ff3acbf458a8fe3a6a21 64ec19f51c117e5c70df5ae1e9b5cc83b55760a256eee107a4c7b1ca8ba2c3c3
mpw-007_MM1_tiles.json
where every tile belongs, and its checksum
13.5 kB 4db89b7f3d21f7fb9fda610a818d799f96e3cd4724473af3db79ee9f3e4e6e4eddfd5bb4 b99fc38abeeb764517c09ad7738355802e2b2a71947414b25dbba5912e64989a
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 tinytapeout 7576×107760, 0 7.5 MB3b11b1093293c3ae41528812a8dba61e392ef360fc67b0321751ab45dd3eeb90
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 8.8 MB21453c4085216ef23425b2f49a0d1b5a22c82542472802f6dcba7295276a5584
A3 microwatt_mpw7 7576×1077615152, 0 12.5 MB297783bf74b82c1570199c08aba64148e28eb863cfc90e7c58b05ef8ad157cd4
A4 yonga-can_controller 7576×1077622728, 0 6.0 MB99a63a7e4a5f53754b7cf237970c367b0edc0bc294915f9692d9175c691b2a2c
A5 rocketalpha 7576×1077630304, 0 12.2 MB71c160820eaffd5825141108562a66642cba5dd12c17b1c4745289d21bd5f6be
A6 riscduino-dcore_d3_3 7576×1077637880, 0 13.1 MBb831cc40577959d435cbcc66c022e854613b30d661dff0c81bf0c7568d9fd4ea
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 13.1 MB3112d080b67c6c00d549eb239ce3a4efc154ee7cb7772bc1dd98be1ade52cc41
B2 riscduino-score-s4-3 7576×107767576, 10776 12.5 MB792058010f99bf8fd2b3d1725310a3acd8dbfeca351e2f6da73c647ebe87eb66
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 6.2 MBaece45e738c422582025483637286d77b51bcefb6ffdf6ed46442520b9ab930c
B4 sramtestvehicleg3 7576×1077622728, 10776 5.9 MB4b95e9f2362430ecfbde46ee61af95f4c2b282104f7d547a6b532451c9e2d4a0
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 5.6 MBa5505e21636fb946bb9313e4b45bf5184d31473df3bdcff0ef9ceceefbb6d00c
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 6.9 MBbaad51feee3dbbe5701434519ce3dc697d9968311e96e250f375ce87832aac68
B7 reram_crossbar 7976×1077645456, 10776 7.1 MB8dbf86f8930b47c24100dca5da56c5af81fba7dc48f60e052fefddf65aec4ed1
C1 trainable-nn 7576×107760, 21552 9.5 MB27101fe6622fcaf7a96921478c4ace73f6d20c5d0c91fdb395af23d10b75ad54
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 5.6 MBbfa206f38189bd2decf367036b90c8b4b9a7510449eacab136b2a66130dc21db
C3 bitcoin_mining_asics 7576×1077615152, 21552 6.3 MBb3690cae0076ccba77fc742e58389bea555121509fafdae47ec0180e65db9212
C4 yonga-mcu 7576×1077622728, 21552 9.4 MBd3f93f7ec6bec80f71ba3fac4e816435d06afa971252bb3d17daeaa598a5a423
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 15.9 MB9f4423b387694e9af52a0dfc166ee5eaee7e61660f94f52721b010e48419aa09
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 12.2 MB2db310bddd90a532e58c571a63f7630b0d797ae58954f7bea66805cda0d30d9a
D1 mixed_signal_circuits-jun13 7576×107760, 32328 5.6 MB624fa030d9a494cf00dcebd9ebc37c852c1d81f6baabee3149d64a449d910a0b
D2 fpga_programming_management_unit 7576×107767576, 32328 7.6 MBe0a40564cf99ebf850fb0199cbfdb96162b99dc38c80b630aa17f6931ac46a49
D3 warp-v 7576×1077615152, 32328 6.8 MBe210ef8091c642fd668122cecac300141903733fa612be347f1d0f44c12100bb
D4 leaf__mpw7_ 7576×1077622728, 32328 7.0 MBd01389a3e9532dd63b353e8f08c13b5376ff8e7326840ee72efc44ce33f4e86f
D5 in_memory_computing_sram 7576×1077630304, 32328 6.2 MB6811ef2aa61a58fc5ce91d677b1135c0b1e77b4a19084d3c64c1509d4b261ec6
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 6.0 MB94105078714fa125ea208bf38889bc6f3272bbaffd941db72e29551fb251312a
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 5.7 MBff52c22b76da7e0520b042cf11b8548ebb685482486ff18ba425ddd663893630
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 6.0 MB5e217ba5b5c9dd58954f1219bc3b0b5f6034b0e4be80389ae7bdc014b750c038
E2 in_memory_computing_rram 7576×107767576, 43104 6.8 MB3496419591bac9867c03bc2ce7395395f2c61edbb18816c5b59ba37c9c153d11
E3 rram_imc_v2_flat 7576×1077615152, 43104 6.8 MB3496419591bac9867c03bc2ce7395395f2c61edbb18816c5b59ba37c9c153d11
E4 topmetalse-dps 7576×1077622728, 43104 5.6 MB4b683a45009bcd3465dd8aafe12c907cf338787dff045bc2d0e86f9155e7b4b7
E5 chaos_automaton 7576×1077630304, 43104 16.2 MB68a3654c8921b4bbf7baf10def57e68972d18fa189f248f967fc0a40586eb704
E6 hehecore_resubmission 7576×1077637880, 43104 9.0 MB890f21261e1dedce2c0f599201536b7ad43d98cdf2c1b184b98f509160c27007
E7 waveform_generator 7976×1077645456, 43104 6.6 MB4a1e50b330e5d29eee506141b0cf8f5ac6af68a71b491b1ff45c926261b2a8b7
F1 rift2core 7576×111760, 53880 6.4 MB2795fd668cc45e014db3394e20f81b235c9a4db66310ba122339c17da3114e6a
F2 rift2go_2300 7576×111767576, 53880 13.9 MB845c260d0ee88f4f21bbb777549cce209931918e2d0f9cb0e2a59122fa4a2b39
F3 sloci_resubmission 7576×1117615152, 53880 5.7 MBc59da6af6d1209d42c10a9400bf349d140d18d3272829c816051f1d3f63b5610
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 6.0 MBf1d7915acfcde8bbea904f9b0eba6ea4a5688645cd1a60036bc5e8b0a793512f
F5 crypto_aes128 7576×1117630304, 53880 7.4 MB0fa30093afa486ef6c862120a996693021ac268b4ae5fba1cfddb3c4828954b6
F6 graphics_controller 7576×1117637880, 53880 6.6 MB07ad1d516cd497ec7d9d7a6c874d920ae7057c7106e790adf8da1b9c749b9306
F7 prga-test 7976×1117645456, 53880 11.8 MB5a1bfb0488e7c1f075b9ca0f5a91be3b9ca41dd560df68263e238744d9dbd292

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-007

Resolved: reticle 5CS8016AC, run mpw-007, 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-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/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.