mpw-002 / MM2 Metal 2

the MM2 shapes of all of mpw-002

40 of 40 dies rendered.

562,698,424 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 (586.8 MB in total, largest 25.7 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-002_MM2_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_MM2_reticle.png
the stitched reticle, full resolution
313.4 MB 8de384759c98e46b9995ff5b9fae61fe391756f3caad77320be1a9fac16d631a25cc418b 162f054d486ff69dfaf4eaa685d88376c1fae7ba1625206d89bcd444de26f754
mpw-002_MM2_reticle_overview.png
the same image scaled to fit 4096 px
6.4 MB ef1911821a86ec929f756e35f4247010c360b25bfb0046af11d4565a8809b92dc137be9c 3f81033aa42665776900140cf1cab09b561a6f2d2f5012cadcd698c4fb67dad6
mpw-002_MM2_tiles.json
where every tile belongs, and its checksum
13.3 kB ee2d547d1009f40634f2ed1e5ad178349fd00dada460c9ad58e87fe80460d424c4084309 bef359805d79c6537fe78e376358534fec0eb68b69b9a6a7c63e4dc8500b0e1a
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 17.7 MB25d594602b4ce1a4f1e7a8ec3bef471d9f35c9d5bd716e1a6a0f2f70c68320ce
A2 wishbone_can 7576×107767576, 0 15.1 MBb067f6b30588539bee3a60d5b910261e2c87f37283c863dcc320f1478904ab7e
A3 Analog-Neural-Network 7576×1077615152, 0 7.4 MB46f13103a15363d9c38a590b63101d4ed6c6b394e89286b869cde6a36eba5822
A4 caravel_dsp2 7576×1077622728, 0 16.7 MB0a421205e97ae609fea5025c666dbea3c9b84680d254a0b159188c1afa8f0520
A5 eFPGA_v3_caravel 7576×1077630304, 0 20.3 MBdd59a125b32b373e4eec81bd328fb02e2599edba9751ad8086aaaded6b882fe4
A6 opencryo_testchip 7576×1077637880, 0 7.6 MBa1fce381ef4e1e667ab8c4b1d91c314d751c6016cb802a5362c65158508155c8
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 15.3 MB97c133cda09b3d68445368e2ed321e6fef813f47bee905110dd003434a1676b7
B2 lexicon 7576×107767576, 10776 16.7 MB4961d82b3e635fd74dba94a224f816b8d54857c20f49f5c5d8973dbc9e4246b5
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 14.2 MBb0079822013f312f3ff32ebf5cd10e421547ee357004170326096f7e19872709
B4 yonga-lz4_decoder 7576×1077622728, 10776 16.0 MB16b345eaa41e4c6b268f6fb0a3675f8ef9b53c9cd7e73289119aaf367b38d9c7
B5 Potentiometric_Digit 7576×1077630304, 10776 7.8 MBd70439d8e7e3e9d18d06bdee659afe251adfeb785d9afbcc9d5b59e99954e64b
B6 fuserisc_ver2 7576×1077637880, 10776 20.3 MBc56d1da6db204aaf96f36e1eb6eb8a6a9c3c066db0eb47197a617b817383da47
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 8.3 MBec0cec7337e98bcdd004a18c56b4c40ed08148e0d55b8af996f334ff01e305d5
C1 Comparator_3v3 7576×107760, 21552 7.5 MB06a5b6db3724f29f3a08e206e614a2c50cbf465bc67def23fbebd369fec7761b
C2 caravel_multi_encode 7576×107767576, 21552 14.9 MB559ee351c102a4aaa77e6f1952ac05131909f966965e5a80977db08e1e549e19
C3 kasirgalabs_uart 7576×1077615152, 21552 16.0 MB844f0d882f7e0d8ed167e37d9a22a067e7b3819b674c362d08d465a0ca17f0ec
C4 Automatically_genera 7576×1077622728, 21552 16.0 MB4758380d6596961217c36d6bc0a027cc71a58d8f074e8d0fdbaaaff91ee8579a
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 21.2 MB257dbb3384670ca1bc63c01657ad34b799059d94d6cb4f851c123fe4baff15d9
C7 caravel_azadi_soc 7976×1077645456, 21552 17.1 MB00d56df314978a48de1272099a75d83097c1d60c9760fa8a7e2ca5372e2e9149
D1 caravel_dsc 7576×107760, 32328 17.0 MB25a2461824af3609e6c3ac6a3cea5710d5419826f5053981678a0592c1528672
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 22.6 MB6deaf84bf803714b6907b1149e8a4ebcde54a346d4b6a500d62854809f4c0764
D3 caravel_user_project 7576×1077615152, 32328 16.0 MB0ce83fb49cb020962f7e0a5c0985af42cd7ccc31e3b2e4669ff13c204a7cbcdf
D4 General_Purpose_Band 7576×1077622728, 32328 10.9 MB7f21f9a23a03d3fb32ebe7881bd6064cb15bb095bcc63eb626dc4b6958ac4c87
D5 zero_to_asic 7576×1077630304, 32328 19.7 MBa39a449e27a567335938ba85660dbfe5dc462d85f00a3d8c714aa25247658689
D6 fast_gcd_for_large_i 7576×1077637880, 32328 25.7 MB98e93ee1292fc870e8908b6d3429e664801e1b676bbf96bfade6d4617ab80fcb
D7 Columbus 7976×1077645456, 32328 7.5 MB7a55e8715ddf94c8169119eb8ff2b315e44b6f99e2817e812cb8015c5872e0ff
E1 Space_shuttle 7576×107760, 43104 16.9 MBa8c14617e46071db925c94f57871eecb65004565e46ea61917f429c57b61ea10
E2 subservient 7576×107767576, 43104 12.7 MB4b1ec4411c63909c1413756025c0199318bb024c9942996a7af752b07a5f9f57
E3 caravel_analog_fulgor 7576×1077615152, 43104 7.5 MBd24de9b85a97495bbdf3b4229e6ac32e3715b4661b65f656db98be08a6de70fd
E4 bandgap_reference_de 7576×1077622728, 43104 10.9 MBab7e73ac175239601c476ae28123083931681facc68af43553dc30633b3134c0
E5 renml 7576×1077630304, 43104 20.2 MBdd7eeb9c346ee03ffd35c79a0f079b74a95c3495f989fdceb9741d3a9ad7260a
E6 io_expander 7576×1077637880, 43104 15.6 MB6655a95d9d67bbb38f933b1826e15f7d5977786042f6271b06719d4ee6e64042
E7 digital_pll 7976×1077645456, 43104 8.0 MBbd2bd6887976171b269f1ca09237e61c415fa94c3e61a42540b993f4f3a314d8
F1 zero_to_asic 7576×111760, 53880 19.7 MB57ee233034335fe4690a46f63b4b9f960cbf7aa50b4d69d04f54e91d384aaf25
F2 caravel_fpu 7576×111767576, 53880 15.9 MB7c23ab4477f6d6838aaec400fdf148dded3ba227199b2b8ad2f86a41dc05b673
F3 BrqRV_EB1 7576×1117615152, 53880 20.8 MBfa8e2e373e9724a9d7d87ab90d2045b32d44dec098a133620fc4e1c16d6d81dc
F4 hilas 7576×1117622728, 53880 8.1 MB4dea6dc4b214b07cda2e18ec67650e3415389307146556c388d8b6845ea64f31
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 7.4 MBd7c5fdb3572b762d7c84e4d3d4713a3deab038ef97212645e091ce98a9dfa914
F6 sha1_engine 7576×1117637880, 53880 14.6 MBdf48d192bc67d07be3aea9441a8e89cebf0cc09f37affc2481744c80dd87d3e1
F7 caravan_openfasoc 7976×1117645456, 53880 13.1 MB625de06d811bf616b3260946568be485239482b074ad8b586def9ed47a2c40c3

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 MM2 on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask MM2

Mask layer
MM2 — Metal 2
Process step
124 — Metal mask2
Drawn layer
69:20
Layer on the mask-layer GDS
41:0
Fill layer
41:28
Also
Metal 2
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.