mpw-007 / VIM3 Via3-PLM

the VIM3 shapes of all of mpw-007

40 of 40 dies rendered.

56,672,570 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 (18.5 MB in total, largest 837.6 kB), 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_VIM3_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_VIM3_reticle.png
the stitched reticle, full resolution
14.5 MB 36d4d9557ba30d8c177409eaa421b6fa272048d2b15b6b37d5b9b59be99a8320166a2e5c 213984bc9c2fc5099067b1c8518f178e4f860d16227a1d99d656434664291ef6
mpw-007_VIM3_reticle_overview.png
the same image scaled to fit 4096 px
668.7 kB 4018de30a13cede39527dbfb3a8a1a27dd83f31f236f8c8364e804f80e764e1316222f64 2420e26f890e6adc07718dfbdfb6d4f8b05a096d35d7e77c056c9569f203a1f0
mpw-007_VIM3_tiles.json
where every tile belongs, and its checksum
13.5 kB b73b1cc80bec8771eecb232a2f9bdb44ce6523e00eebba8550f117e1b2f4b86780ec54da 2a3cfd9bdd4cb234ea05b509d8cb039033c867d925a9cae2a1e8ece97c315b34
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, 0 443.3 kB1fa397e327d0ebb686fd79ce4a4553349d98c013e9b133bdfc33bc724e834382
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 492.6 kB25c79a1c65ea4263aef56fc2114746ec41eac24011820f72939763c924da9780
A3 microwatt_mpw7 7576×1077615152, 0 587.9 kB364ed0dff46a827ded5b9447d7b1ab3dfc36f967181dfeddb3bdfdf974211e48
A4 yonga-can_controller 7576×1077622728, 0 357.6 kBee74ff4a1be5679b88def3b892ed73aba3adcd02f747a7e23f828e6077c57942
A5 rocketalpha 7576×1077630304, 0 596.2 kB03131075e948abd9597ba7cd9fb412d4047e83155cbf9e713d956225f6a293a8
A6 riscduino-dcore_d3_3 7576×1077637880, 0 627.2 kBe0a772a9dd5c11910032430e30966fef306a27ac6313784602ee93448e34ccc1
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 649.2 kB007e7871e3aa45898144825bca0c4f4419159dcd9c97fa4c4f70e78f77418d38
B2 riscduino-score-s4-3 7576×107767576, 10776 589.6 kBec6b87850f6ac58e7263b9ebbba97ce366fb083a9bd18ecff4ed52d2b06b90b7
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 380.1 kBb04fb710a03098c5194b51036f4608795c15ed6500096906b24522a64e45ecbe
B4 sramtestvehicleg3 7576×1077622728, 10776 325.2 kB6ab1a73f1087754c01a29fa21cc83f6def6877edea6e540e909f73c479f9930e
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 330.6 kBbd527f1321326cd0fe3b44f9a7d69096321d830cdc8ef154b848792999ea2664
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 396.3 kB71e04a6cd748b9c7b478d305655edb4c76eb672fcc85fa95e68ec037c60aaf51
B7 reram_crossbar 7976×1077645456, 10776 837.6 kBf1d9120c7c875162d4b532d04828b48e5c1cc5d3ee5fe267211a7dcbff9676c8
C1 trainable-nn 7576×107760, 21552 494.3 kB027c2001607def26c9531bf72d7ffc54085fb8abc96c1e134d69928475bd6d83
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 335.6 kBaa8860d5fcbc026a3859307ff16dd0981015f6ccc48ac56b31cd0f20cc9ff874
C3 bitcoin_mining_asics 7576×1077615152, 21552 361.8 kB32b2506a4139e212de08d46260e99aa9b438cb122f1aebb11b6dd3d42126bafa
C4 yonga-mcu 7576×1077622728, 21552 542.3 kBb8f823bbfc67e72ef952f75edf7baf9e1ead68be2cdd36ad916a3873ceef04b0
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 635.1 kB2673f8f28af76d5676f738b045a7f824540997fd3c73d077fd4a65259a5b3ef9
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 659.2 kBfc759b1ca5b8300feffb5a1862e031838f73794d3eddfa27dcf2a7ed95d04659
D1 mixed_signal_circuits-jun13 7576×107760, 32328 325.1 kB44d2dbe476501fa964192fce63bd090f8fe7dfde6bc630dac32a63d96f01ab17
D2 fpga_programming_management_unit 7576×107767576, 32328 409.9 kB628e151ec32d094aaadc39fe2532081d58c4f46e49a43ddcedf5ce6af2c0e52b
D3 warp-v 7576×1077615152, 32328 388.0 kB47990c7a7ed3e232f5f1df36184eaae1c50f61f3a8d54b5805dbb8e7365b0ac1
D4 leaf__mpw7_ 7576×1077622728, 32328 398.5 kBb60e8c4efb5946c3a73d92e49bf0b24b78b4fadcd5291211935ed4b13924d2f4
D5 in_memory_computing_sram 7576×1077630304, 32328 363.7 kB01081c6057934cc1dfc01e4396274224ed92f3eff7ac261b2b32fdbdebef7435
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 354.4 kBa2ace52372d1ad344b62f6a4d17654f8e3cfb9013795c2621b66a56de2620a1c
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 357.7 kB9f1c56a118d6513d29c7fb79d265d0ce818abd639ef9d3bda052ed1d5cd20829
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 356.6 kBb7c60e9ac3b4bc5c5fa3bdb2b65ed0914f895bf08d3a9744777a7531ddfeec78
E2 in_memory_computing_rram 7576×107767576, 43104 384.2 kB59cbeb42682fcbe6549372a8345d3b62d96a29fbbae342ebce0b585eae05d115
E3 rram_imc_v2_flat 7576×1077615152, 43104 384.2 kB59cbeb42682fcbe6549372a8345d3b62d96a29fbbae342ebce0b585eae05d115
E4 topmetalse-dps 7576×1077622728, 43104 332.8 kB585732c33f89f297a8297cf11c326a694ae1eaa388b8ad98010d07961ad3ebd0
E5 chaos_automaton 7576×1077630304, 43104 633.1 kB8860d036da40fb64f61f751ae5aaaafa49fb7a62eff47aeb89cef9559a88e092
E6 hehecore_resubmission 7576×1077637880, 43104 536.8 kBc089e8c9b8173efdec00ab9c5834fa30309dddf1ccd92f4a30e960d262fc8d3c
E7 waveform_generator 7976×1077645456, 43104 387.3 kB9b0f011a5c8749ca924088335e75c9ca95dc3d77febd5dd35fe9166721298872
F1 rift2core 7576×111760, 53880 382.8 kB3d53486a8e70db7483cdbc6cff4d61fd04ececaf7fd7ec2d8fe661224dbab07e
F2 rift2go_2300 7576×111767576, 53880 596.6 kB728403e12684fdf19713d97853d3f403c94bbb5fd9048d14023637e54277cffc
F3 sloci_resubmission 7576×1117615152, 53880 337.0 kB1dd2ee8d8c7b270bcf4ef79b2ee95208bf86e18abedb700d1646fe5ba29847af
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 360.2 kBad0ce99d2f7f8827cbccd9e8f873b54fbe4ddead61d4a54bef37421b4aa3ddea
F5 crypto_aes128 7576×1117630304, 53880 409.4 kBfb65a70153f7d1099e622d66275d03a63b8c850a234c1a3a36ebc4b7a81d952f
F6 graphics_controller 7576×1117637880, 53880 439.7 kBd9b3841fd4c55bc51cc26782b6aed09159f424e9e7f9919b5ba629eaa67d06df
F7 prga-test 7976×1117645456, 53880 482.3 kBad5d00c032cdd3e0599b1a93ebe4242674aa65df992be02c31032527a6ac4818

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 VIM3 on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask VIM3

Mask layer
VIM3 — Via3-PLM
Process step
144 — Via3 mask
Drawn layer
70:44
Layer on the mask-layer GDS
50:0
Also
Via 3 (M3→M4)
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.