mpw-002 / VIM4 Via4

the VIM4 shapes of all of mpw-002

40 of 40 dies rendered.

9,825,036 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 (28.8 MB in total, largest 894.5 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-002_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-002_VIM4_reticle.png
the stitched reticle, full resolution
11.7 MB 39e5c8729eca1606d4804d56db513780ac9ce7b8ec4413fda58d21cbac8dccd7f332208e 9d4b6634a08d94e6da0c2b977512a198cb7b9e886294fa0057ed40c9b97db4a6
mpw-002_VIM4_reticle_overview.png
the same image scaled to fit 4096 px
583.7 kB cfb4a16aa28094603fe5036645815a15bfc1993b9ce1ccef37047598041115d5945308ab 311ea0959bf712bd67e3e2191b4a3d51151148103cd84286765c3214aaf55c6e
mpw-002_VIM4_tiles.json
where every tile belongs, and its checksum
13.3 kB 6073b2c37e962f5382555eb45e184f201c818f07beff99e6cd8673481a78b62e7ed9e555 1236785eaf5491783d3e1c1c2d508ff71e0eb1dddd72994a407a23b490bcb1aa
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 axi_dma_using_spinal 7576×107760, 0 720.7 kBfb09a0f66a7df8434c27434a9467af16e5ace64e610553c1fd8810b1f7867a10
A2 wishbone_can 7576×107767576, 0 724.1 kB089a21cc927a5b4003a3c1e7638599ccc244c0a766e94db468fd73cb66178547
A3 Analog-Neural-Network 7576×1077615152, 0 661.5 kB7577b5df932f7753cb73512dde4c004436fb5b47e343b13b46ad8ebd7c980624
A4 caravel_dsp2 7576×1077622728, 0 718.6 kB9bdeaeb28485ce5b7e67adb25009e9b3212db9f29b8c0422a1ec074c49612d19
A5 eFPGA_v3_caravel 7576×1077630304, 0 724.5 kB17fa1ca1c2944fc9fe48b1abf6b7bbae7a3c7f1fcc2bd9c948e971bc636b5e25
A6 opencryo_testchip 7576×1077637880, 0 661.6 kB27418d344e979a88527a6574b6e5cda833add2037c5a7c2ced2870782e3a8eb2
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 706.6 kBd1ff7cc9ff5ead258b544ceff11212bc2ee3b893a30e382ff7db7bddfd771eec
B2 lexicon 7576×107767576, 10776 704.6 kB65947599428fbf05018533f82ff60b7ed745403c6eec9bc126ad5c586e72a3e2
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 717.7 kB2409765897d48e433a4de70c086ebd2f05142463bed2c68d056c0aea90ff8a80
B4 yonga-lz4_decoder 7576×1077622728, 10776 719.2 kB4fe729c78f07ee91b1567b1f5f922ca151138f294f8c6aad7c5ad07a679b7bd3
B5 Potentiometric_Digit 7576×1077630304, 10776 661.5 kB7577b5df932f7753cb73512dde4c004436fb5b47e343b13b46ad8ebd7c980624
B6 fuserisc_ver2 7576×1077637880, 10776 722.5 kB15dfc2cbd43d63b1d01f2869f2d17042f8cc264536ac32cc36a38c496a2956a8
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 687.0 kB047ce6482b1ed84abc01f7e465a9544b8c5cf70dc82ccb85bdee446e7daea4b2
C1 Comparator_3v3 7576×107760, 21552 661.5 kB7577b5df932f7753cb73512dde4c004436fb5b47e343b13b46ad8ebd7c980624
C2 caravel_multi_encode 7576×107767576, 21552 720.1 kB49ab8171a9f7e31aec9551795fc8090cc7363836e667ce6dbc57ad7eb2f1f3e1
C3 kasirgalabs_uart 7576×1077615152, 21552 717.5 kB9df71f980b38412b679fde2df707ee5c8649de752d306ad3fa4a7da2b769ed45
C4 Automatically_genera 7576×1077622728, 21552 715.9 kB591dc4c39cdd540eee6f52a627836033e2dd8fc6f399fc1f7b19954ddebee466
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 704.7 kBad5d77ba8b70fa4833d2bd6e5d83a640e8396231ddc8cc5cb01b9e806d496ac9
C7 caravel_azadi_soc 7976×1077645456, 21552 730.5 kB9edda7b5b8040519e03a21d145e21776e555980a3d214cee2cd331a27b3dfb43
D1 caravel_dsc 7576×107760, 32328 719.1 kB8cc46ca90608846d718231fe17d99906f6d61c236b9bb35d21fbe20925d7e491
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 862.4 kB4377f94c555d64a0cd7d2d2e396f43df10292159e665931a758d46da779df7d7
D3 caravel_user_project 7576×1077615152, 32328 717.5 kB9df71f980b38412b679fde2df707ee5c8649de752d306ad3fa4a7da2b769ed45
D4 General_Purpose_Band 7576×1077622728, 32328 715.6 kB6864565f1e81a86383deacc3c470164a508b93ecc2b2fe3170b6689be54cff1d
D5 zero_to_asic 7576×1077630304, 32328 719.6 kB183366232b3ffe6938f538ae52ee2ff42480dc1a0412a5b27ccb734392675940
D6 fast_gcd_for_large_i 7576×1077637880, 32328 894.5 kB490089f2cdf3d6e0d6c3d01aed4b6152dd6165f2f32471bbb11d6154cd0c53bb
D7 Columbus 7976×1077645456, 32328 684.1 kB658ad4af5093ce2c172abd86211e72947d27508d6058243ee223a09888a2c9c3
E1 Space_shuttle 7576×107760, 43104 722.7 kBdb4973590b8d09ef27a79abb9a91d84221b1c5610a4b0014826fcc6f6c58e865
E2 subservient 7576×107767576, 43104 715.3 kB4f2ec3bfab1a38251e3cfaf352e3f5767a442a3088975a531f00a48c26d890a4
E3 caravel_analog_fulgor 7576×1077615152, 43104 746.5 kB6634e2998eb068018551880552560271993cdb035b3bca746935a563fa93b064
E4 bandgap_reference_de 7576×1077622728, 43104 715.6 kBafa4273a3981a0a30ad6233e222d3e19b020dca3f8e18cec1e68125f1b87b3be
E5 renml 7576×1077630304, 43104 710.3 kB7b4b0c3e9c586b69434da7fb30dfe628b35e80482b201c7fc573ca0ae02d74e2
E6 io_expander 7576×1077637880, 43104 715.9 kB61709d0e66c329bb5ee3d6f79bcbe764f170a292b601730b1ab80fcb63e9851e
E7 digital_pll 7976×1077645456, 43104 699.4 kBb2d10b25d1e82073905208e3be3254bd3d832f4fe1d06d4a2d74fc6d6b145f99
F1 zero_to_asic 7576×111760, 53880 724.1 kBf6d60fb752af622bbf18baaf87a271b4f1eea0e1f36314361cf8e8c621c36efc
F2 caravel_fpu 7576×111767576, 53880 719.8 kBb302db8d35394bdfb875c0d4184127f8fbb44a62f569be0a18d364bbf2fe53bf
F3 BrqRV_EB1 7576×1117615152, 53880 709.2 kB8a0d46ab41786916f374a4622a8559933ec7b31d44359f2d8ed18ec9ea9fd01c
F4 hilas 7576×1117622728, 53880 666.4 kBb13fe84081083e6eec7b623f156a46bea171fd1c90c802c3a03f9f010b7cd84b
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 666.4 kBb13fe84081083e6eec7b623f156a46bea171fd1c90c802c3a03f9f010b7cd84b
F6 sha1_engine 7576×1117637880, 53880 723.4 kB1ef87021891afafe81a13b399799f4c3432f48fb57addcb28a707051c26ba2ec
F7 caravan_openfasoc 7976×1117645456, 53880 697.4 kBcbf63ea0657a67e8d27356b1c5627c43a4e360366804b8a5a1f31949b5100c8f

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-002

Resolved: reticle 5CS8007AC, run mpw-002, 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-002 — SKY130 MPW-2

Foundry product
EF MPW#2
Reticle set
5CS8007AC
Run date
2021-06-18
Submissions closed
2021-06-18
Projects
56
Fab lot
4205819
Identification
confirmed — Lot records: EF MPW#2, lot 4205819 = 'SKY130 MPW 2'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-2 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-002/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.