mpw-007 / LI1M Local Intrcnct 1

the LI1M shapes of all of mpw-007

40 of 40 dies rendered.

259,859,978 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 (346.2 MB in total, largest 18.5 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_LI1M_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_LI1M_reticle.png
the stitched reticle, full resolution
274.4 MB 30be08ce05fa808a7743986e24950770508e698e5796013d0742a803b12f9fc122c21247 12c4636058aa2a217937195114bca49f600bd245482819bea607ac3c05b89820
mpw-007_LI1M_reticle_overview.png
the same image scaled to fit 4096 px
5.7 MB 5ad11841f56b867a331240b1afad3d145801b5da92c402ffe5261ba22d9ecd611caf8b05 9ea811502a2e70e4d70664053771dc5656b83dc4166aae3b6528b5d909d8b574
mpw-007_LI1M_tiles.json
where every tile belongs, and its checksum
13.6 kB 6d744af16c5f87987181677edc206f09b5436b70801492d774081b669d61c9ff69424928 53643b1942a70618f6e1da7560d0e82421759a73bfb03b0696b6a3b2de6d6686
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 6.9 MB41f4102da4235b70010dd68fd6d9f83d798abb83ea6f621d3c7d5ff2f3f5237a
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 8.6 MB7decd5eabb046b1b0d5f0d0a7c9c4103dd24fcceecdd3ba547fdbd4a3697ccca
A3 microwatt_mpw7 7576×1077615152, 0 13.0 MB05f98a8c0c139166bc70a6671c50aaa9b608a7117f80ce0ac05a08bfc57b4fd1
A4 yonga-can_controller 7576×1077622728, 0 5.6 MBdaf9e2421ab4abab3772aa1538dd83e5786b53a3a4ffa9eb387c72aee9a34518
A5 rocketalpha 7576×1077630304, 0 13.6 MB3c9f1fce66418aed770b3684541f6f229fd71b02b8ce28ffd6376b8035058a00
A6 riscduino-dcore_d3_3 7576×1077637880, 0 14.2 MB9a7fffcfea977a63b064dd1f03df442441ac0a75c2f4e7c22cccef79edc1097c
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 14.4 MB77311713dc1a398040c84f5429b2042330f7ee38fcba6092fe7b6a5bb24bdf5d
B2 riscduino-score-s4-3 7576×107767576, 10776 13.4 MB5006f607f7ac5cb14f8f14d57e00fadef92cc47e9aff812bfaac8182044e8e5d
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 4.6 MB2bd1ef8646cb493b5bb4a4684e8d49ef3b931d6fd5ac0786e2554e8062acb0d7
B4 sramtestvehicleg3 7576×1077622728, 10776 5.5 MB029460732c1e6c3acfe72c040b5497a5a9813e7c14271277b943a6c87c0dccaf
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 5.2 MB44269092c30ebfe3e6052a1a73bd81c43f6a3f6458c868b65d40bec4b7c90e51
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 6.1 MB8990e9a3329e10257be39b82259e631b3e927929eddeda4015f97cf248670088
B7 reram_crossbar 7976×1077645456, 10776 6.1 MB6fac2b30938457d1ecde4dca7c6233c4472672931693b6b056a71aa6b8f0ca88
C1 trainable-nn 7576×107760, 21552 9.8 MBe4a9c658e783104642a352d71d5f879cadf804c5b5ed7f5ca4c4fbaa484a3fa6
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 5.2 MBfdba661b0bb7f56018983a5807a9d2c4b9effa5df53ab3ce740d48831a9d92c3
C3 bitcoin_mining_asics 7576×1077615152, 21552 5.9 MB834fa9e11718a75d94a47a02ac02d65f8a22c0778e546a16e3c39be189a952d4
C4 yonga-mcu 7576×1077622728, 21552 10.4 MBbbd702d0c7c4bdbb36d0ca675c34aa42466648f41b409e487dc2b8450bf1f1b7
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 18.5 MB5f1edced07f8d5a000f73daaeb14613f154cdc90149c0fa106fcf919db1bf067
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 13.1 MB3230f7d466ee2c43b7f39b5153fa3d54fe34b33e47b4ae4dab58349d87d5fd3b
D1 mixed_signal_circuits-jun13 7576×107760, 32328 5.1 MB155ac29703bb06896eef66f26c8af0d21c9605414e2b2ae651408fd154c92a52
D2 fpga_programming_management_unit 7576×107767576, 32328 8.7 MB137b9c59bf5abeb96dc920f0fca5bc3b93dd1b77f38a8c11eb0c2060d6287fcd
D3 warp-v 7576×1077615152, 32328 6.5 MBfa5a3593e8c030f4784698761c0f73698320a595fc7142ce0f435364cd5ea0c9
D4 leaf__mpw7_ 7576×1077622728, 32328 7.3 MBdbac68578d635ba40874615336a056def19967dfd2a9a9afa7befff6b57c28af
D5 in_memory_computing_sram 7576×1077630304, 32328 6.1 MB7e645b5a7d618728b91cebaabf3987825840851a0375e8e344ac9b08ce2a2c8c
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 5.4 MB8e0ce819fa3ccc706cfb7205e2380fbcb3b38140b97d8d664df5731067e52b1c
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 5.2 MB23b25b4572d3908442614950114c18fc2ae58e9f4d0cf9648cab3115a3d10f29
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 5.7 MB87864e8db4b1cac9c75cb0c091bb62cac796c5122d8658e6a63759536426c70e
E2 in_memory_computing_rram 7576×107767576, 43104 6.4 MB29b33232badeb0d7602a74519cfd37b879e58bb115f4d6c92b3476e0c09e71e5
E3 rram_imc_v2_flat 7576×1077615152, 43104 6.4 MB29b33232badeb0d7602a74519cfd37b879e58bb115f4d6c92b3476e0c09e71e5
E4 topmetalse-dps 7576×1077622728, 43104 5.1 MB34f5db8313d843addaff16ae69d2a6d47ea57539d22fd25e72e42c033a7a8776
E5 chaos_automaton 7576×1077630304, 43104 17.8 MB0a80a5e1de44eef1edf390a6b5ca13974cb07f421dc841df8e34db4746ed92f3
E6 hehecore_resubmission 7576×1077637880, 43104 10.0 MB31cf1ee875bd41b82d55d0bec92aa45cdbc97a648a9db2b7d2ee255f0bc740a9
E7 waveform_generator 7976×1077645456, 43104 6.4 MB7ef7e43952b15f4b10cc1b3149f9a849be4d69870229cae97e45cad8527e9fbf
F1 rift2core 7576×111760, 53880 6.4 MB4b4b76c8fc4466c74312dc71384ef726491e460b9f17ad5f2b7dd2a8edaf9e0a
F2 rift2go_2300 7576×111767576, 53880 17.1 MBa580644b6d5053decd57c06920c500c2da239a9e3f382754282ebc069a726d95
F3 sloci_resubmission 7576×1117615152, 53880 5.2 MB8d079a83ea9ee7d0f4ab2a2279a379600deba877c78701b61827bd3525161d4f
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 5.5 MB01eed778f521d0cfd3883cf1e4559374e77e83f8146a3da40441b0180d952aec
F5 crypto_aes128 7576×1117630304, 53880 8.3 MBbaa30dd0047aaef498415542d9a3057dfa28019c6793a7971790d1205f5fcefc
F6 graphics_controller 7576×1117637880, 53880 7.5 MBeb0596bbc850607d880fda28dc01681c05f641f69d0639db4f7a1700b1afe9d6
F7 prga-test 7976×1117645456, 53880 13.7 MBd95ac447dcfbb56bf30481dbe017631d1400b65bbe59a9d50d2c15b020e7d5fe

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 LI1M on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask LI1M

Mask layer
LI1M — Local Intrcnct 1
Process step
102 — Local interconnect 1 mask
Drawn layer
67:20
Layer on the mask-layer GDS
56:0
Fill layer
56:28
Also
LI (Metal 0)
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.