mpw-007 / VIM4 Via4

the VIM4 shapes of all of mpw-007

40 of 40 dies rendered.

11,465,175 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 (30.6 MB in total, largest 1.7 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_VIM4_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_VIM4_reticle.png
the stitched reticle, full resolution
13.4 MB 2fe8be8af5148750869115a45a3365fe2e247df7e462d11a3559f9a31697591195dbc6c0 d8824a6be0a3d0e1569e2f044b9d78a1f2f636ef832bd6a60ede335f21ea6ef2
mpw-007_VIM4_reticle_overview.png
the same image scaled to fit 4096 px
706.5 kB 5c8f1e9f0d5b43edd6b50e5b9cfc25670dfda2a18d4b99ce58e419e2bef453c281c9727e b5f28f91a76bf2255ae2ff5bbb5366414f1bee9d99e80d182952617b08303280
mpw-007_VIM4_tiles.json
where every tile belongs, and its checksum
13.5 kB 3f941524e38bc24bbb7a7f316432fed7bea9d09489dba811c1b938cf539d58b407093734 40babed3e66e5995e294cad67aa8dd88847965c66695876dad966c2ca813b05c
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, 0 710.1 kBa0754cea462d6f89df38aca897195a1aebedf698bb6fa3b0e919ccaf18779eb4
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 731.0 kB48a4257d021c851c9a36cb2ac216290ca365eed9e74e40d9b755ed5ac4060b27
A3 microwatt_mpw7 7576×1077615152, 0 729.2 kBb201273bae8f1f1f92d24922dd2da884d6b3f1f1fbdfb918bf3edda6515c4615
A4 yonga-can_controller 7576×1077622728, 0 733.2 kB34d2d4d1e4de00bf9f6296d151ad2c48c84611293391bb124068e3c543dc2a11
A5 rocketalpha 7576×1077630304, 0 710.4 kB7a09d5435aab7518df18018eb36522c2212b513f2ec7b4f7a2056580ae4a2baf
A6 riscduino-dcore_d3_3 7576×1077637880, 0 787.3 kB646a9b02bd8d39c1febd5037009831e44bbe8abe3802cbf856e757217ec7749e
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 790.6 kB73899310385a7a43f11e3147aa8fda5195a5f2bfa8bacaedb46f76bb127fe1e3
B2 riscduino-score-s4-3 7576×107767576, 10776 787.3 kB11c9b5b42059c332f260b73171ce4c43c98104b3e9486c1f69bfcff0f0768f08
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 880.4 kB2db16d1d1dbcbc3277a8b0ad1ed48912fed1ad944b52de25d461d3b199024379
B4 sramtestvehicleg3 7576×1077622728, 10776 670.0 kBa71f8f63439fdd3eb2f83dab02096f09f1d71c2f4abf7b3d30599fca444105a1
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 696.3 kB0ab616a5f6102cd663941ab38c113261bdcb7d9b48aa0a573a4011b928ef3602
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 728.9 kB6ac131e1ee81afb8eace71b01fb56a811bf591cc18b949d6010afabaac4ea5d9
B7 reram_crossbar 7976×1077645456, 10776 1.7 MB2505e7bc35283570b11b9e13c0871384e2c07eb74fed1e3b07d8e8013d46a7be
C1 trainable-nn 7576×107760, 21552 730.9 kB9540cd851a4b713da4b1eb2acebc8b7b29f7915cbdfceefc1b361987f6b61cbf
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 850.0 kBce3f3ceaeaba2bd656317b8ed473cbd4b2da0a0859812bacd27ff33938998799
C3 bitcoin_mining_asics 7576×1077615152, 21552 732.0 kBf439393926dbada788ecb79aa837297cee3205ca2dd7122d140d4463660ca183
C4 yonga-mcu 7576×1077622728, 21552 726.2 kB50b10832d7c12a0f4ef1b0ec3009fe5ff7827c93dc345bcdd78ed6e2b519f21f
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 729.5 kB60957591fe7187490f9863c90db043c6c66a950648dd378172d1a364c5833fba
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 723.2 kBf6f2ca2deef31bc6dfb3b194a2ba783119800f6e7e17bd298894b2725b99f7a1
D1 mixed_signal_circuits-jun13 7576×107760, 32328 669.8 kB99c4cc1c1596071b36f824e775b1bbe7e1adbc993807d0cdcbbc185c24b70e16
D2 fpga_programming_management_unit 7576×107767576, 32328 727.6 kBefd65c1771d89e7661348f5ec7784febdbed8a9d96321f9062cea0ec5e52f12d
D3 warp-v 7576×1077615152, 32328 731.3 kB2113639a15d2abf4cd70f69039471349e58dd9fcfb1588ec529caf336cc2dcd8
D4 leaf__mpw7_ 7576×1077622728, 32328 730.3 kB451c0d01aae69415ae0e8e3e0781101605e86277ef21cf3ab0eb53eb6044592d
D5 in_memory_computing_sram 7576×1077630304, 32328 738.2 kBaf99c65cdcba2d3c36218d831e6d82b8b340f30814b204f99ecf40f4ac46f19d
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 733.4 kBf35a3790071f74bed6d752a059e7c24fe8739feb92b0d537496083db0008dda6
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 738.9 kB11cc5bb1cb0a9c3d1f6a50a6591a04c838c218bf3801cb3f1a3c02ac913eb1b6
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 731.6 kBec52be7289e323ff18e838cf1734c8f86f714a7f74f7ab523afce0cd0a012349
E2 in_memory_computing_rram 7576×107767576, 43104 734.2 kB6fc15d88ec25fed828cd335dd1770710e9b35893eb1294f57af4232766d43d19
E3 rram_imc_v2_flat 7576×1077615152, 43104 734.2 kB6fc15d88ec25fed828cd335dd1770710e9b35893eb1294f57af4232766d43d19
E4 topmetalse-dps 7576×1077622728, 43104 682.2 kB7c09bc9cd97855f1781569583e99938baf18f62ff12e003733c9f687e85a6b97
E5 chaos_automaton 7576×1077630304, 43104 729.8 kB172228350d06de8796536b67660a706395e252cc5326e511467a5ddff04dc51f
E6 hehecore_resubmission 7576×1077637880, 43104 731.4 kB89843bd10ac64a621986e39bac237fa4a5c3fe629808a28cc3ca666ebe77f31d
E7 waveform_generator 7976×1077645456, 43104 747.0 kB455b86d3970e3f6d30261b3b78ad4c4a1ea10ef6a223f779e3bcfca71758f3e6
F1 rift2core 7576×111760, 53880 739.8 kB7a5a069bae084db1b40064651298e0bc384a389edf77ce58951428deb1dfea01
F2 rift2go_2300 7576×111767576, 53880 726.5 kB3efe0fca4b8def38608ab92f95b156cb27a995c5d75f3cc824a2e1665cf1ace1
F3 sloci_resubmission 7576×1117615152, 53880 697.9 kB40f2e9721306bfc7fc99866593ef4dfc4a1f726229acc8435ccc68e8ddd2f3f0
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 736.0 kBa1c648ad9bce56f53d599486992a2147ac9bd6c4010e87d187f64173f29d180a
F5 crypto_aes128 7576×1117630304, 53880 734.0 kBf876c5b4188d5c704a1d3b62fb880868e6a5ac9d72c5333387a9069b3a6978df
F6 graphics_controller 7576×1117637880, 53880 736.1 kB58cc566444dabbc0d1bdb1ff2473a14df7adb1d0aaeaf59efa2d98ac545b54c4
F7 prga-test 7976×1117645456, 53880 742.2 kB91eadad75e8c1a4b27497ca2725e68973520af01281aee7366c028486faf7883

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 VIM4 on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask VIM4

Mask layer
VIM4 — Via4
Process step
159 — Via pad mask
Drawn layer
71:44
Layer on the mask-layer GDS
58:0
Also
Via 4 (M4→M5)
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.