mpw-007 / CTM1 Contact

the CTM1 shapes of all of mpw-007

40 of 40 dies rendered.

502,239,544 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 (177.5 MB in total, largest 9.1 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_CTM1_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_CTM1_reticle.png
the stitched reticle, full resolution
137.1 MB 0a4d446a772656797dcc57d7f37e538dccdb9a8976dd90da74b29275034aea133c4e1dd1 b85315cbf909b38aa89c003fd4816a05f4aab9f619510207ed7dc09250db29bb
mpw-007_CTM1_reticle_overview.png
the same image scaled to fit 4096 px
1.9 MB e8b4682c1f8f6f690f214dd581217dbdb424d047aed407814c613161197b2ec6fa374462 44c0ca0ea856f5d52f47821fabb9dd3bbdcd73ff896755a2479e42f2479701f3
mpw-007_CTM1_tiles.json
where every tile belongs, and its checksum
13.6 kB 48c13688513e511faac3e25f7594004746dd159a5f0bf4e4a23567cce064ea10b39398f0 c7c079a162e8b318c5c834615422da7dce3fdef2085d9a2c075f8124b381853e
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 3.4 MBed51e898a26eb01891db59b05bab8cb70e54a37eabb879afb23eab24750a916c
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 4.4 MB249c58158cf52436d7f2f4eab7f5b9aa776f2a493625a5b532fc180553432729
A3 microwatt_mpw7 7576×1077615152, 0 7.0 MB1648bf52526fb00a33541df6c1595dab38004aa016155eaa8670f38c83550cff
A4 yonga-can_controller 7576×1077622728, 0 3.0 MB39da70b5e6ae0328893720aa6915b875336e87ab368b30fa3caf554af402cc7a
A5 rocketalpha 7576×1077630304, 0 6.7 MB20a08f22795d2577515e6adb5a69b6f955c266030f8df622fc5e7d167e2bc161
A6 riscduino-dcore_d3_3 7576×1077637880, 0 6.8 MBc3c2d84fdfdc7f553757517a728df81d36e7bafa6647c8b6168703d666dff6f2
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 7.0 MB7f6db610649f22412a31907c2144d090ce4327c6a3a5743fa8442375cc6c99e7
B2 riscduino-score-s4-3 7576×107767576, 10776 6.5 MB22e2a599436e8134d1957d64c001293695cd86bdf76c684475b3f749ce49b9b0
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 2.4 MB891b6d3579927a375b3ec79d7efbc9cf93f2ef95b513b56021c9953839bdce6e
B4 sramtestvehicleg3 7576×1077622728, 10776 2.9 MBf71cacffad443b70d79228ff4102a6540e4a2dac36f70d0d164c1f9f4f1514f2
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 2.8 MB7df4f84b95849e120a2a9ef064628892898ab902e247ce67fa53dea8fe59c00b
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 3.1 MBbceeaa65f7fe6a480362f0674f3fb917ecf7738219e8216787d0ec132fb93a07
B7 reram_crossbar 7976×1077645456, 10776 3.2 MBa7769a62e989992f582f099ad96f7275def5a1f94d8ba24c466f641448a997a0
C1 trainable-nn 7576×107760, 21552 5.1 MB3bc9f442ed08d4bf57ee2bb9404e8c8ef2739e7a62f2c57c688d368ba29e6d54
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 2.8 MB3eba6e4477ba954fb4d0b92f221197ffce863421515e634b1a1e8f331326be1e
C3 bitcoin_mining_asics 7576×1077615152, 21552 3.2 MB3d37de0d0f850648535a5d0e3783de3a2585f3ebdfa2d79e6adaa9727a5b5fcc
C4 yonga-mcu 7576×1077622728, 21552 5.2 MBd6e77dd779fa375658d4600d7038f0ea74b9417dec78c5c7046133edab5b4b1b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 9.1 MB60f8fadfdc72bacef12c963d55cbdfeddd9b8c803fd2efac7da9dcf88548f054
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 6.1 MB2b2808dd24cf8192f507a75ed80d68536a43de13234265a1141bf14f4214e014
D1 mixed_signal_circuits-jun13 7576×107760, 32328 2.7 MBedf4f75be7dce8bcd1992f124b0dfce1a4b5d6a3caf2f28c926bb3045556b3ee
D2 fpga_programming_management_unit 7576×107767576, 32328 4.3 MBa268d576096d7ceb375193bf1309ac19da4c83e464805b6bea2c076b7889e435
D3 warp-v 7576×1077615152, 32328 3.4 MB1b3e696ce31b8c4cfb5b3b7aa92fa0fac60622179f224309f47878b864570101
D4 leaf__mpw7_ 7576×1077622728, 32328 3.8 MBea965b9a406a444a3f4192dead24b90868cfb340da5746857c50014e353974ce
D5 in_memory_computing_sram 7576×1077630304, 32328 3.3 MBccaafb027f5c4902c11b8ee3b07b098fb8fc71719ed8b78160c1b6228b43010c
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 3.0 MB53c7466eebbd37fee74d8459256219791cb693873db8eda4d34f74347ea845f2
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 2.8 MB9347959c7e30b96ec23cc1771ca10af3a9fb662a1452eda3256c051d38e337ec
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 3.1 MB7c9d370359cebca555ece86a68706960960b0e75e8e11f2652f6b5c069a870ba
E2 in_memory_computing_rram 7576×107767576, 43104 3.4 MB29127a8c1e83d5b7ad72561b2da92f807e11a6a288d159021f2eecda46590fe0
E3 rram_imc_v2_flat 7576×1077615152, 43104 3.4 MB4b1a3c9028c5ccdce060481f000556b61ea9cac8679a1acd904bb103fed67b34
E4 topmetalse-dps 7576×1077622728, 43104 2.8 MB731e8dd3397d184a10a507564d40cda3a39c29ffd3a799a2d93b4116f34125c6
E5 chaos_automaton 7576×1077630304, 43104 9.0 MB92e5b8ab1a35e3cb380669262924774f08187cdec01e982563dc8969964ceb77
E6 hehecore_resubmission 7576×1077637880, 43104 5.1 MB0c625b82fa4267b7a3ed41d96a8100335d82e46f2ccf0ef791f6f99faac2a7de
E7 waveform_generator 7976×1077645456, 43104 3.3 MBe0a9322911e7090c0f58e4ff0c357d82ba82a8ab9b6814b1e8e5b5b3fd2ac61d
F1 rift2core 7576×111760, 53880 3.5 MB3cca81eefcc6fe6e53457b3e037b19f750018555d6ffc680241c24ed964b3e4e
F2 rift2go_2300 7576×111767576, 53880 8.8 MBab9b1b5bf0d0cf976d33cabcf8169bb218d79addc7561cef0dd99b4220102e5c
F3 sloci_resubmission 7576×1117615152, 53880 2.8 MBc8308b861d9b14f3ec50b0fd62de6c2db593d0bd23214889875382353710c683
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 3.0 MBc92f128d07f235f81b06d121ac8bb8b61a82bcbc986804411f5b31dded77c9a9
F5 crypto_aes128 7576×1117630304, 53880 4.3 MB946364d36771905b6cede8a448c1cc853268d0de05fd651fc2e3207d0efdf148
F6 graphics_controller 7576×1117637880, 53880 4.0 MBcc6b99471301c844b767ea670ba50bc8b57047fadfaa1563c87c4f8ca4321a78
F7 prga-test 7976×1117645456, 53880 6.6 MBcd44ab869d942763e0d1895c472000261eb73a4fece932f8ea22544be56904d7

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 CTM1 on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask CTM1

Mask layer
CTM1 — Contact
Process step
107 — Metal contact mask
Drawn layer
67:44
Layer on the mask-layer GDS
35:0
Detail
CTM1 = mcon (li1->met1)
Also
Via 1 (LI/M0→M1)
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.