mpw-007 / MM3 Metal 3-PLM

the MM3 shapes of all of mpw-007

40 of 40 dies rendered.

230,417,355 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 (213.4 MB in total, largest 7.9 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_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-007_MM3_reticle.png
the stitched reticle, full resolution
104.1 MB 4eb9cbcc7146e1a2f1dcada9ff9b45df4b1018e5f41461c63451fab1a03182996412e06b 700357de32bcc4619c46f4a59452b9f469bd2e69a495e96a1c87d0f8d3aec6fa
mpw-007_MM3_reticle_overview.png
the same image scaled to fit 4096 px
5.5 MB f83ff12273de5f65aae33160177890b7e15bff95cc4199545ac9416af721abdc51debca0 f9c5021bc524508768796196909b676e5ed35733bcbce75b93eeca7b7fd1ae2f
mpw-007_MM3_tiles.json
where every tile belongs, and its checksum
13.5 kB 967e680322ce2dca4db5d74f5a0891e480cad9deab020381bfc6e8988f251fbed30619d1 a39dc03c9d6ef50e15dd00339776e630d6b22eef59bdf1343944d0fac8bc39d0
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 6.2 MB89df13e30af28c2dcbdc051380d94df85fbb03aab86792d7d73abf98d766b145
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 5.5 MBbb6a74e7c80a8435603e0ccfc9123afaedd768e8135792adb8a04ed907dfe686
A3 microwatt_mpw7 7576×1077615152, 0 7.5 MB0b756927a2e37a73ed4490beda8f28c7792ac27839cbc8bbdcdb2ccb0c012693
A4 yonga-can_controller 7576×1077622728, 0 4.0 MB66f7bb5f1eb8470f12e30fa04f562705863259f4e762016332fc0b47b24d8726
A5 rocketalpha 7576×1077630304, 0 7.3 MBa609e669baa16e16d4a8ff70e005658b919267c2cbb33c0ca44e6aa916a1c893
A6 riscduino-dcore_d3_3 7576×1077637880, 0 7.7 MB62f76f49a227b8c159e717fb8ec91eae61e3fa5d8cf5c765250321b831e44e23
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 7.9 MB4f40745c684f68cdd0881cdb20fcd6da0ea21f5001e087e0b1b54844cb69ee0c
B2 riscduino-score-s4-3 7576×107767576, 10776 7.4 MBedd594fa58db02b01af1a12eaa0279dcf57c0a95ab05ba54f63fff091f239e65
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 4.2 MB7862c8728efc30c4df1bbb37e82261078fd3177fa3a34c9f24ef574b0ba08a02
B4 sramtestvehicleg3 7576×1077622728, 10776 3.8 MBb12ad8af89f11b707980c0270f9fd16e01c645b830e03797c962796d265b8a5d
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 3.7 MB62d942d6bfc145135df626e9be6a58c14b5b65706a47c68ca0f595ebff86e038
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 4.5 MBffc45f36e513e2d230ef5409c4417f8d866c110b8f98c3629f268af80473aadb
B7 reram_crossbar 7976×1077645456, 10776 6.0 MB4aee1230c6bf44d4c5215513b5eabbaa5b792a273d60a68d884dbc78170ee47d
C1 trainable-nn 7576×107760, 21552 5.2 MBb36446792f52632e59cc045013131f8c00c16b42b98c8b7408e03cc608f001b2
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 3.7 MB50823ce7ed3da2c80542b1516ac902d6853af98f4b20ac84cce2fb52d78861cd
C3 bitcoin_mining_asics 7576×1077615152, 21552 4.0 MB2edf568934e1fa65618de0639442855a405c570805415cb40c7c00c026ed1c57
C4 yonga-mcu 7576×1077622728, 21552 6.0 MB120c4d75a8cf48d3626f19c2f3243b2d682f5d31c7dee522ad14e3bd706afe11
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 7.4 MB6e516517d06d7b85bdece40c8fbb88c3ab39e5938f9b0276eda4312aa120c2ff
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 7.3 MB518607d595ca29689a8e002aa102e003921d7192d82b376af1f753e39b4dd1ab
D1 mixed_signal_circuits-jun13 7576×107760, 32328 3.6 MB2f072d2b555cf1aee69d7feb2de41d1115aa521d2a1c79661407f12ad2b99782
D2 fpga_programming_management_unit 7576×107767576, 32328 5.9 MBaabada48a8b1efa8d878746b426633621667ad0589fed1f5e088390684b61ab1
D3 warp-v 7576×1077615152, 32328 4.3 MB6691528ee133364212334194ac1d814bba18d2cea36e7782f4fd4f4323f09fe2
D4 leaf__mpw7_ 7576×1077622728, 32328 4.4 MBf5d2fb38aab7cbb7c1f81950ecadcc7148dd14ffec4af242b521ca98e93de040
D5 in_memory_computing_sram 7576×1077630304, 32328 4.0 MB0da383162e5caf40252dd53c0d9e56ec1ccb3d3eda296cd5e00c929809ed062c
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 4.0 MB3ade932854bba486f12ffdb68e29aff35ca498616747e4011d3b1e9766e1bc0d
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 4.6 MB98cb3320137f378ddde01c235aaa572685dcbb21e7e9428f3df6beab78f2c681
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 4.0 MB8437c24fc9e7980605138576c3ef633ef742929abb4fab4901108f75c60ba2fc
E2 in_memory_computing_rram 7576×107767576, 43104 4.8 MB17ac8fa4d8d261bebdadadcb6c2e4cf4c2ae700ca05281efac2069bd7f1bee6d
E3 rram_imc_v2_flat 7576×1077615152, 43104 4.8 MB17ac8fa4d8d261bebdadadcb6c2e4cf4c2ae700ca05281efac2069bd7f1bee6d
E4 topmetalse-dps 7576×1077622728, 43104 4.1 MBd62faacc30f944e645e1b1c824a8d89b71dc008857e7f541af80677a7d2783a5
E5 chaos_automaton 7576×1077630304, 43104 7.8 MB026e5386f1184644e8ad63f078caf5e8ec6947451fc1cc565146978acc656bd6
E6 hehecore_resubmission 7576×1077637880, 43104 6.7 MB3ad2c8ce51fd66d2b3d46b80fa23ab76edf960d15e4509270e65689a8139c06a
E7 waveform_generator 7976×1077645456, 43104 4.4 MB6c72b6fc3c8faa13a1316b836f713179e90eaed9706c3b7de6d5415750e040a5
F1 rift2core 7576×111760, 53880 4.3 MBc4b2a45ed234547039cace4b94409197c1920ba2c2e9dee66d43406b20762215
F2 rift2go_2300 7576×111767576, 53880 7.2 MB473fbcd3e07ba085eb57bd752a6c79d2c6fed5b5ccece7cd2a590b655def217c
F3 sloci_resubmission 7576×1117615152, 53880 3.8 MB2dfb06839bc606c8cc4cfaced1beac850f50027fbbba0d8cccc5ff4d8f8a8c6d
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 4.0 MBaf3fb9cc807fe9cf87e57edf5b2ebd0223b719c268cd05a21020494489469c45
F5 crypto_aes128 7576×1117630304, 53880 4.8 MB45e97528e9bb54ccea045648e1176b1dd9bb13ba4b80c122a2cc44082eec9fa2
F6 graphics_controller 7576×1117637880, 53880 6.2 MBcfc200e9e44b7daa0f3d33f23257e16929e3a9ac65e0b6dfde147d027e8512be
F7 prga-test 7976×1117645456, 53880 6.3 MBf55c020b73c5281c76e4573c616f0146bec1191bb9115f528bc9b6458e442fc9

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-007

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