mpw-002 / VIM3 Via3-PLM

the VIM3 shapes of all of mpw-002

40 of 40 dies rendered.

36,642,681 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 (19.1 MB in total, largest 1.5 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-002_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-002_VIM3_reticle.png
the stitched reticle, full resolution
14.9 MB 3e5fcc28d889dccd9ed1365d5e8e7367a4bf0ea279a852511f20b401499f8b0ce6a36b32 94e288791d06ff4bbdc06812c311aaf46e05c8ce498bfa6f54eb27e951d2097f
mpw-002_VIM3_reticle_overview.png
the same image scaled to fit 4096 px
631.8 kB f314b2eb1ea7af0790638d7dca2bf1cca4042088dcc2f3dea4053a7e7103c81ebadc82e4 9fb2c9a353a3c289814530734b106ba699bd27ea88f1f98a4dc0c0f20d37f992
mpw-002_VIM3_tiles.json
where every tile belongs, and its checksum
13.3 kB 915ce598176a799dbd7c474a847b020b04a1f99ac677547a47113b7c6d441579d593ca86 83699e3fcc2dbf4648a13d9d8015f389671f064481b7d2ecfeb91ccc1c4196fe
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, 0 407.1 kBc415ee4458095aa5b5e621cdb1595c97eb863ddec30708f9863ba91a8b441da6
A2 wishbone_can 7576×107767576, 0 415.0 kB05c5a54c55144fe518890a184fd909a44c8f20148c663b0c28168cc5bc83a172
A3 Analog-Neural-Network 7576×1077615152, 0 291.8 kB013c71ab5f3aa9a7405aa61e310fec2c75b0806c3b59f26f19b20c032af5600b
A4 caravel_dsp2 7576×1077622728, 0 396.3 kB04fea08e927fab08ba3c3213ff054082fd61f42f20e5201f5934834ee8876052
A5 eFPGA_v3_caravel 7576×1077630304, 0 1.5 MB44187839454058629bc7b77850f897ffecfaf6bd3d15ee232dfe9ecee560e07f
A6 opencryo_testchip 7576×1077637880, 0 291.7 kB5aeb4f7deecf61564fd43e976939bab3b1aca7520fe82d4cd11d84fd29a5a906
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 356.8 kBb865e5e64e11e6730215a6005e4ffc5087b4489e0bc040666e122bbfd8f3b380
B2 lexicon 7576×107767576, 10776 545.6 kBca59c021ea8ab81fe9f05afc49beb11070423e5b4710a754833f7bc4644548db
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 473.1 kB4b6f526a901bb2476c9cb099dc48ee2820b3e0f908a21671e2bfd1d8ad56befe
B4 yonga-lz4_decoder 7576×1077622728, 10776 400.2 kBf729c666a8f3069148530b3b4c8c9fe578e0b705116f0e9b9342c3c78efa0b52
B5 Potentiometric_Digit 7576×1077630304, 10776 295.8 kBdf6d1f496fb8d5fe9a3a455a61fe98184327f5e54c26e500ea58c2695cf3b09c
B6 fuserisc_ver2 7576×1077637880, 10776 1.3 MBba88028a284d4db4f36092df09d92387e08549d7f9fae5d74409e623400731ae
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 316.1 kB43b519290d91af85954364851619972445f25c717be2b0c2b60a77098a8a1a2a
C1 Comparator_3v3 7576×107760, 21552 291.7 kB290187dad6614d5529b75782085fe3f5e97783908cdbed56da8a1030699d4b70
C2 caravel_multi_encode 7576×107767576, 21552 400.7 kBa29bc08febcab8f0a01e9218c6bdeb5238fbdc06d9ad26e13ecc0971b92d84cf
C3 kasirgalabs_uart 7576×1077615152, 21552 390.9 kBe835d2930f1a5938752d087d265a1770a346d93cfcb6d782f816f2e8142f611b
C4 Automatically_genera 7576×1077622728, 21552 383.0 kB632bb1eb9b988b409981bc2916b0a2f87aab1c803c436467540d673554f569d0
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 499.0 kB09a000d25dc1a433bcd92a04fb020f3462a3e3ee778cd2041a452162b68715d3
C7 caravel_azadi_soc 7976×1077645456, 21552 420.5 kBd66c6c58fa0f67732750dd89113056890ccb0ecd5d7578b0970c883109d38ffe
D1 caravel_dsc 7576×107760, 32328 399.1 kB0eda3a3d951db45844eb2fe6f3640dc3f84896adb397df2cbea0984fa674df77
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 791.5 kB80e4a0ef00e6c7960e0a710c38d422567d2ad270a932f7e57db086b2aa01946e
D3 caravel_user_project 7576×1077615152, 32328 390.9 kBe835d2930f1a5938752d087d265a1770a346d93cfcb6d782f816f2e8142f611b
D4 General_Purpose_Band 7576×1077622728, 32328 381.3 kB84ab7b947d9e58631e40b2e8738e371eb1ae64f27b94ba27b6458e43eed4a835
D5 zero_to_asic 7576×1077630304, 32328 419.6 kB31d430fdf37e6679bf6d62d5cdbb29c146b5970f781656ec226349adc4620fff
D6 fast_gcd_for_large_i 7576×1077637880, 32328 1.2 MBafe909757c7806a11227749054fb19fcb66b4a74983ded850a013ac7d4d669e5
D7 Columbus 7976×1077645456, 32328 300.7 kBc532a306cbbb0cd7557dce34a17c7f3ebf6fe21b991e4b8ba217ab6aa3586e5d
E1 Space_shuttle 7576×107760, 43104 442.0 kBf030e02c23c6fb09ee862f4f1c9fb8e4116a114c3750cec823c9c9fd21e0254b
E2 subservient 7576×107767576, 43104 397.1 kBea75d7f57a230364d8064b23a35d1ceeb913679b091ea9e1881696988d7199f5
E3 caravel_analog_fulgor 7576×1077615152, 43104 303.8 kB64398597609c9cbc151790f520c4dbaa0af3724d7cd4997140f1b0688665fbbe
E4 bandgap_reference_de 7576×1077622728, 43104 381.3 kB391f29982cb24b8fd6418a28404385195d2232dd1614c06906f3dfe88806d0f3
E5 renml 7576×1077630304, 43104 458.5 kBf1b0a83c191af9e5c9c5979f5b59d43453048553f2e99e13dae87aef8dde5da3
E6 io_expander 7576×1077637880, 43104 383.4 kB4853c1e9dd46cdcc206718039f3a4bf008be4ea105cf00ac24c840feb47159f7
E7 digital_pll 7976×1077645456, 43104 313.0 kBac37be8840d56317d8ded964a62f49f9730605a46075c9902a65f3ffc071e4a7
F1 zero_to_asic 7576×111760, 53880 423.7 kBe4a159119b930ea9b6e0ebb840d4b2bf26ab1b5dadee6db5c47ef1ffadcbc162
F2 caravel_fpu 7576×111767576, 53880 396.1 kB7172f986af791312d771b1129d5bfba35e2fb4b9cc784d60e3a90a8408fcda93
F3 BrqRV_EB1 7576×1117615152, 53880 676.5 kB0b24f838bc17d62989ef3d027eb45b3900afd6ccc0b6e11f5453f0a38dac6781
F4 hilas 7576×1117622728, 53880 298.7 kBc3ccb614af8c392fbc03514d1d625c309e74b1751d28b82ef8be090a77d0e63d
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 296.2 kBd48596fea264b92dd3ea04813877a857f0640c1182a79530db0b8f3bdb1472f8
F6 sha1_engine 7576×1117637880, 53880 421.5 kBa0f536867cd392a3e52c00d4bd1b99cd6660a122fa0284c4708b79b867eabbe4
F7 caravan_openfasoc 7976×1117645456, 53880 499.6 kB7457f4cf98e3604ac876c9785bfff8872be013ac8e74b1f4383e5d98bc060f37

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-002

Resolved: reticle 5CS8007AC, run mpw-002, 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-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.