mpw-007 / P1M Poly 1

the P1M shapes of all of mpw-007

40 of 40 dies rendered.

208,226,829 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 (376.9 MB in total, largest 16.1 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_P1M_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_P1M_reticle.png
the stitched reticle, full resolution
235.5 MB 276bad842da0734eadfc4d0a569189b498dc4f147f33a27d6c450db3a8a41216648a2229 44338134b20505c772bf4c45fb1abfb76aaa62bd5ee9651d748ef55b31e20c6c
mpw-007_P1M_reticle_overview.png
the same image scaled to fit 4096 px
8.2 MB 71529318078c0bced723f454d5fa4b7167455bc7205a16328cdb423d801356d974bc5081 a164c33de6950bbe792e0753ffaa40a6c509e0ad0f6464413c8a28e08b5c99b2
mpw-007_P1M_tiles.json
where every tile belongs, and its checksum
13.5 kB 80990836472b5463d8ed9aa76eb06662efe2bf18221f5bfc485eac3c8231bef7a5932b51 237a2ce239b3a541060c6d9c2bc90fb55fe58e0dc11df9205070d8a0f0864eaf
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 7.8 MB26da910f003f2e1f0434acc09f4d372688b1378c62b81ba7d52af7b938aa38d4
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 9.6 MB9ccb855dbc72bbb29edf083486c66ded502fa036264b14ccc110bf1da90bbd6f
A3 microwatt_mpw7 7576×1077615152, 0 12.0 MBe3351dfa89be48d587bae944590c19cd3982bcbff646465aa067917a308edf10
A4 yonga-can_controller 7576×1077622728, 0 7.3 MB4c284b56d868cecc3e375053a3e217e080161908a9390af5fac0c7ca6ea9c624
A5 rocketalpha 7576×1077630304, 0 12.7 MBf0f89e99a78a99d75f730096668160f609a2d7fb0ecb1c7f118c08a308d32f7f
A6 riscduino-dcore_d3_3 7576×1077637880, 0 13.8 MBb8beba927b3b3c046531025eb82927bbdc16518bb0afedccc6a63206c48c8807
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 13.8 MB5c58fce96489040ad0a4ad1810bdef3315af64447e3709e36171c4bcdf5fd6b9
B2 riscduino-score-s4-3 7576×107767576, 10776 13.0 MB54af3f559dc29a9cf034b4370f6f806afdb2664c3288d2ebaeb382bc2d8efba1
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 6.8 MBdd1854b4b60290ea37b51ac95cea8e7a7718a5aba58b7d19d4006a67b5eb6c54
B4 sramtestvehicleg3 7576×1077622728, 10776 7.2 MB695a8f591b0859ce2751b1a98528fb46b328189248066dcab0a510911b01a4bd
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 7.0 MB17e17f4990e282142bbd823b1fa9a37deea15d2d8d28ace490e83fdf461a6979
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 7.6 MB2a3d86e225ca3186669d0927dcbf02da2d1c742cb70c48ede2ddd5aef9ba9056
B7 reram_crossbar 7976×1077645456, 10776 7.6 MBd2030db8d2fd5dc1a36e17ac5b973ab0bb8e70eec2be5d9cf3beb4289e94e669
C1 trainable-nn 7576×107760, 21552 10.6 MB9e2563327ed86460ec63d5b488d7b7200624e840e412fb6328b41b478b2fd28e
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 7.0 MB74c6b2a7209fb21528cfdae7943db9aebd7dd5ce6d699884b84d166e92a9e443
C3 bitcoin_mining_asics 7576×1077615152, 21552 7.6 MB19fa109001fcb619551c61c31d8932240eca718670296032d8d4dbb3862009cb
C4 yonga-mcu 7576×1077622728, 21552 10.9 MB06e9a8a6081db4666fa8d1a4f718f33597fcff70d7cb73cd0b5fa962411ca7d7
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 16.1 MB725ca49cb6633eba127e3126a1f31f638283d466821a1a520c9c6c3ad1c8953b
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 12.2 MBe5dfc805fd76c2b3e068c283d527c7cc7717c2388366d69c9264fdf3b918e52f
D1 mixed_signal_circuits-jun13 7576×107760, 32328 6.9 MB5be5e1b62db6dc186c1b7608bb1572fc2ce0caa31f758d981d66230237f35ced
D2 fpga_programming_management_unit 7576×107767576, 32328 8.8 MBa9cdffcf159e738a394009df1982254578197e6576a489b0a01a40e9edf4295a
D3 warp-v 7576×1077615152, 32328 8.0 MB158f15203b68ff5e865bc4a1e98e9158fa4e7d630ff049ce5a7e99ece7a4eb9f
D4 leaf__mpw7_ 7576×1077622728, 32328 8.7 MB35f996395d2191636bcea1d2dcf720223772f3f3e495756d3f0b892672dcc812
D5 in_memory_computing_sram 7576×1077630304, 32328 7.7 MB0ffd25b184468a465de46f85ef8fc40de37cf5f3a611b76517169a002e1382f3
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 7.2 MB89b39eebe7f82a71b323a19c341770bcf87909149b492df87a151e9202ad5bd9
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 7.1 MB5957e060de982509fa12d0b82c7347c72411e5507e24c25d832d687be5e5937e
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 7.4 MBaae8f3d0bb6998ee0384d5cc4078bf2c3e32663d66d074a2941776875c838ba2
E2 in_memory_computing_rram 7576×107767576, 43104 7.9 MB8daa550017286e1c0b82597c6137baf846a9a506b2a3599810ad31b85dd987e4
E3 rram_imc_v2_flat 7576×1077615152, 43104 7.9 MB8daa550017286e1c0b82597c6137baf846a9a506b2a3599810ad31b85dd987e4
E4 topmetalse-dps 7576×1077622728, 43104 7.0 MB1f0d03373029db6d9dc2105fff18c39f1abee566d3c34e4c748470bf59daec43
E5 chaos_automaton 7576×1077630304, 43104 16.0 MBf83c2d706aaae74fbedab7a601fc8310a32f22ae3b6b0ab4e893424859c7ad23
E6 hehecore_resubmission 7576×1077637880, 43104 9.8 MB611788a0cb8c0335ac60eb563e0b10853deee0c075f8593795c78c8851bc077c
E7 waveform_generator 7976×1077645456, 43104 7.9 MB3b165453e52129207a38fa405c9288f2f2608fd245b06c77d65f39cd7364dd45
F1 rift2core 7576×111760, 53880 8.0 MB076ee4fb465b6aa80bff4ca60a7b0775cd0b87d4e21efe4f9eb8e1b7af285f40
F2 rift2go_2300 7576×111767576, 53880 15.4 MBb255fd4ded3fbda97d549a7e313cefa0deb29ef57f740e1bedfb1041a131acfa
F3 sloci_resubmission 7576×1117615152, 53880 7.0 MBc934ae8d349be098c7912fdda804bcbecbb5bc5f2478ddaf58301ce91cc05559
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 7.2 MBad457b96e145cd4b6088f445d64846520b796815a05b874179bfd7a9bf7a9c06
F5 crypto_aes128 7576×1117630304, 53880 9.3 MB77d4e794306ee31903dfcea83cbc493da71ec1b4d19bbf7feec2be4823b92c28
F6 graphics_controller 7576×1117637880, 53880 7.5 MB3639c40d8a0e18d22d6182b6abe220d5e9dff909689e2da916a05966d80da005
F7 prga-test 7976×1117645456, 53880 13.2 MB87de8988e18ea09959606ba726705123bfcf87343ce700bfede11af24195f251

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 P1M on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask P1M

Mask layer
P1M — Poly 1
Process step
61 — Poly mask
Drawn layer
66:20
Layer on the mask-layer GDS
28:0
Fill layer
28:28
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.