mpw-007 / MM2 Metal 2

the MM2 shapes of all of mpw-007

40 of 40 dies rendered.

542,062,108 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 (597.1 MB in total, largest 22.9 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-007_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-007_MM2_reticle.png
the stitched reticle, full resolution
321.4 MB e929c6a4d5525a96ea471d7160996b7f2630a642d3e44bf3d654b94b879a53445d20bc42 86c679eb26a0dc2586315ba3e48dfd320135fb78be7d7148f259f6f870bedd6e
mpw-007_MM2_reticle_overview.png
the same image scaled to fit 4096 px
6.3 MB 548ec057282e5426e5ed5da726496f447b80fb02746aac8b51e7c7aed1eccbf516ba3515 bd59db029d119331ddba01315c79c732dce136b10d5764abb2185e6dfaaddae9
mpw-007_MM2_tiles.json
where every tile belongs, and its checksum
13.5 kB dfd6c8c20455ba7a9be6fcdd99f0ad0fcf5988bec6ea94d99a9d5961545633313fe65b44 35d32fb662c4b2d4359056abe3b7ff17027db557f68668a90685808314fb7534
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 16.3 MB976cce814d9278ef4e57415416664f73daad23d4832e6bc365b667f2ca831647
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 18.7 MBfe51e9ec508701ac6618b75a0f58727ca1227873d9512e66600ea1c9e523404a
A3 microwatt_mpw7 7576×1077615152, 0 20.5 MB323b4f7489b2d687aa5da395211409a614a76f28c7a6939b285f31418cddb0bc
A4 yonga-can_controller 7576×1077622728, 0 12.9 MB93e3a133a01cf72f4ef428ee49d870e91a99619dbc6121705a5713a2062198d9
A5 rocketalpha 7576×1077630304, 0 19.0 MBae6f52da71c4b3e9e30969b7714f6e7704a7aca05490b63caba88720c64f259c
A6 riscduino-dcore_d3_3 7576×1077637880, 0 18.8 MB3cd5d6c07f12f20bd773551312748ee2307f01ff3633bd6dfcc8b25db513ab2c
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 18.9 MBfa90e591517a163715014536c7effba7397129fcaba96f1bb941977bf3dbf556
B2 riscduino-score-s4-3 7576×107767576, 10776 18.0 MBa7df33fc52d961243d94afe9130ea5733dc2fbf529fad513f813b07fac811fda
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 8.8 MBa9ffd633c003e87fa5bbfd0f23038dde6b16e99ed73919739a8b290dd5896a67
B4 sramtestvehicleg3 7576×1077622728, 10776 8.2 MBbc5009f3e8fca02a6b716c71579cdbb373313765ed95a333a15175477b2b89bd
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 8.2 MBb69457147eca66a23821b112ca1a8454336945d22cf6ac87304b67bde092074b
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 16.5 MB81a1b475217ed580d5a29d921b52551c36f7f5bf52312e29fb87ef458a88299a
B7 reram_crossbar 7976×1077645456, 10776 11.0 MB00e51f1b44d5a8a3cffeeb108ed5bc51a623feed3fcd664c2d5894b40df8f5dc
C1 trainable-nn 7576×107760, 21552 17.1 MB9cc5c1cfe9c56122eb8caf749cb9a645b5b22925ff3ca98d4d8745cfba5a5c1e
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 8.8 MBc002412ded6cee6081c1d9eb9e5318f5f60f8874e305ab964d34c6cb3a610a63
C3 bitcoin_mining_asics 7576×1077615152, 21552 13.5 MBff97a1dbdb720623511b1324edf92d0313bfc72c3e44b53e39db216712a3839e
C4 yonga-mcu 7576×1077622728, 21552 20.5 MBf3c3385901de125d5dd876ccb71d56e6199d133b9e5235af9e83b66882596e8f
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 21.9 MBd7463984ee0f873486be1972c57a599e11e5e2203ba22febc69508b7f9993f29
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 18.1 MB76c1c30378352b38355018fa6d5bb50f40a78ee82941b5725300ec77e84b652a
D1 mixed_signal_circuits-jun13 7576×107760, 32328 8.3 MB7eff26b9b2a92c4f3c6a401aefdedb61a32484a3a6ffc786e74fa0f06698755b
D2 fpga_programming_management_unit 7576×107767576, 32328 16.5 MB401c0d7e5b16413374b8043b84a63740f17e423ccb876bdf144a1e1c4795ee23
D3 warp-v 7576×1077615152, 32328 13.3 MBf8d06cd0dcf00de249e9000f37ce549d5668437d7bf668e6b03f97da5a34edcf
D4 leaf__mpw7_ 7576×1077622728, 32328 11.7 MB5d9786a485e05c41f7bda8ebcb6d88a7bb5268947d8b00c25e24882e35897803
D5 in_memory_computing_sram 7576×1077630304, 32328 11.7 MBdb5415d7520a9ac65e8b1492cf70fb7fce51d8e54a52ff4bed7abcbf0d72bdba
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 13.0 MB5b7c3dca16c2c015da642a45c1767a559c5955c2885fc1d0f3744f999211d521
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 8.4 MB1078893554fdeff05e539b60d631f5e5347c7703bacb4eae45e1cfdd94016c7e
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 14.1 MB4dceba5888993407c99cef09d3c30f0d1e07b4ebeccc58671406c0db293c019a
E2 in_memory_computing_rram 7576×107767576, 43104 13.0 MBbba6c926ef85054c60df34b0761762e221fc6deb9083375454c149a5b7635b88
E3 rram_imc_v2_flat 7576×1077615152, 43104 13.0 MBbba6c926ef85054c60df34b0761762e221fc6deb9083375454c149a5b7635b88
E4 topmetalse-dps 7576×1077622728, 43104 9.0 MB7cf1ca673a700591d178eb05d897e1624c1d35f4c8917230154d6ae8b56b6168
E5 chaos_automaton 7576×1077630304, 43104 22.9 MB4356676e1bebcff40b3332fe54b93da11dbbca47366d959915d650775f935d9e
E6 hehecore_resubmission 7576×1077637880, 43104 18.4 MBbc9cfb3b693cfca85741009625fbfe8cca29d8720dcd6df745cff0abc82dea23
E7 waveform_generator 7976×1077645456, 43104 13.9 MB4d63243ee8aaa1396c525b9450bd0f35e2526a692b2323f6925f2c00bf633bec
F1 rift2core 7576×111760, 53880 14.9 MBb7336eb34f1aba20060ccc67255a6228a3090239a15bf034145dd622f79475cc
F2 rift2go_2300 7576×111767576, 53880 21.4 MB7812abe927c89f354b527e38df662beee2baad5ace84fa8469149203d8066739
F3 sloci_resubmission 7576×1117615152, 53880 8.3 MBa3ee5f67a6f406b8630b1f6c981076a17fa0d247890bde91f11a642ad0cc4bf9
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 15.9 MB4dc04648ed0eeccca27413de5e6acf80556b10a013b17bbad642d7d4ec69f376
F5 crypto_aes128 7576×1117630304, 53880 15.8 MB4816e7b22331aee2f779650f0c5a84d842d9cb9c7d0040a93c3b4d416a25b641
F6 graphics_controller 7576×1117637880, 53880 17.0 MB2497eab0021b77d4b6354c07130c6e8dcbd677b628179dcb1e27cc5712176a5e
F7 prga-test 7976×1117645456, 53880 20.8 MBd18665b0579b61a9937f6b40435bba7d713ef9b68c0a18ef39cbd94dbd54de8c

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-007

Resolved: reticle 5CS8016AC, run mpw-007, 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-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.