mpw-002 / P1M Poly 1

the P1M shapes of all of mpw-002

40 of 40 dies rendered.

217,039,979 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 (330.9 MB in total, largest 15.5 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_P1M_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_P1M_reticle.png
the stitched reticle, full resolution
155.0 MB 1ff73e2db669f169bfade3fb5bef6fad44c24e314a006810901565e04d1f3eec09427e85 6c606365d65d632db8d6bc13dd775eef4a0548f4142293899e9567a1e6859bc8
mpw-002_P1M_reticle_overview.png
the same image scaled to fit 4096 px
7.7 MB e6b437fd8baf20a7028e2b4ae3ce5712ab122b428f212b64191b1660c23aefc680b69f6d ca5133b2d0936cf04059eac512e04db054764b8acaff4b09a3988e5bd5de8b80
mpw-002_P1M_tiles.json
where every tile belongs, and its checksum
13.3 kB b7fbf07f2162be20862df5a329098b38512a8a42592bb4bb2148da5d920dddf4b8762593 f649148daab816ac79deac7e8d8c215ec1720815ec0f05714aed1a9e3677e6f7
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 8.3 MB775124e6ef649261dd1d41f59a6e29bd8d60f64cb9177daf4f13442c62036a03
A2 wishbone_can 7576×107767576, 0 8.3 MB09615234092344e11e749895ea357b722a3a61bfe0d5fb79c9ade9aba7941403
A3 Analog-Neural-Network 7576×1077615152, 0 6.5 MB89e75cea73ce52cce82d5e3c6ebc6b2e1bb71f45976419682b917bb9e4c3c410
A4 caravel_dsp2 7576×1077622728, 0 7.6 MBbff6f773f3ce7a57ce72268a609f226b528b1f7f6ef082358b2143f3cfe8bc2d
A5 eFPGA_v3_caravel 7576×1077630304, 0 13.5 MB559450a0ffc3233fba92950875d7428fd986e5664be9316227af763e35c66994
A6 opencryo_testchip 7576×1077637880, 0 6.5 MBf9a6000ae5a95a54da7f270f447bf9ecca226cb2214793a533bb2fcfe00cc7e9
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 9.3 MB836e11c51a173d45643d87a2f4cea6f210c05a49d266f50609071da0a78e98c4
B2 lexicon 7576×107767576, 10776 9.2 MBd9a877d5dc015d4fa57199c0b555195b37f73ccdd34ff4eee380fb88a9b1841f
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 7.9 MBf2b6d65a0717b236f1f84dd9344ffe24d069ba67b0d02fca882d9a773bf4b098
B4 yonga-lz4_decoder 7576×1077622728, 10776 7.2 MBd25b2fae7f01d127b847ec6372ff428850898058a6049d8f04bdce2fa826c92d
B5 Potentiometric_Digit 7576×1077630304, 10776 6.7 MBa4e4f87738b454f28462b8f461fcab15400c6ff5c119983a817b26ffe3a7f889
B6 fuserisc_ver2 7576×1077637880, 10776 13.2 MB4ed7d23248e44c8bbe7b8cbfdd618fe8adecf5679a7a2f3321b75bb67c80d607
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 6.9 MB8a2df0dd39f313f51be1bd0d122461575e9315abb495763a0eb487007711aca5
C1 Comparator_3v3 7576×107760, 21552 6.5 MB2513102bb53d2c02fa7bd647dd8f7f507d7d500ebc4af4ad852b430d96f966e0
C2 caravel_multi_encode 7576×107767576, 21552 7.1 MBe9cb013794315e048c4c05b6532b1b1c537fa22b0ccc5ffeea3cc827ea7b7497
C3 kasirgalabs_uart 7576×1077615152, 21552 6.8 MB43211b973c6fcf3741d9123f66655ce119aefddcc95da43e170cc174ec780ca9
C4 Automatically_genera 7576×1077622728, 21552 6.7 MB8be91e7874a06886eb7aebd1fd75a085bf39330e32f614063cb58ff11ce64d46
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 12.1 MB3f1d80927fc6c6d13a8dca27e34a1224b776efca8c22478ca01a976c74e72186
C7 caravel_azadi_soc 7976×1077645456, 21552 9.1 MBffc0d26991235ee1002911e14e692d968e47185da08d5cc18856565d487126bd
D1 caravel_dsc 7576×107760, 32328 7.7 MBa0ead346311626f8e9c16daa3555937d0a4cb09c0075d243441cdc1edaf335b2
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 8.2 MB557036a86de80063615e139ae14b6f3c64d973e1e84ec186ce98b0e8c95e76bd
D3 caravel_user_project 7576×1077615152, 32328 6.8 MB58371193bf76afd76f9bd1e0079998a9d823b3d2e7a0d0fed0ba2cecda727db5
D4 General_Purpose_Band 7576×1077622728, 32328 6.6 MB53a0f608dd078262eb75bdcc30725c21388230c3f1e4dc09befbf21ee467dd00
D5 zero_to_asic 7576×1077630304, 32328 8.2 MBe7c43116f5bdfc33fbaef97267ac8630ae2733a5d9864afb1cdd9e52f8f2af0c
D6 fast_gcd_for_large_i 7576×1077637880, 32328 15.5 MB517826c078029e2bba895a44e5d4278be3707e73222ce402788209efd6a54dad
D7 Columbus 7976×1077645456, 32328 6.6 MB83476c915b6e3c81282d49a0b769a6add63fc2251e2c14901f9c7b58f9c4c323
E1 Space_shuttle 7576×107760, 43104 9.3 MB6c884a7f0fd1573a13e2430e92e75a96500e12bf188773fcd91d534b15f90da6
E2 subservient 7576×107767576, 43104 7.2 MB330300f49f182d2bcf12555fb09f72501db20bcb65cdbb05d067679df9e9065c
E3 caravel_analog_fulgor 7576×1077615152, 43104 6.6 MBc85060525de791b3cc033c2240b0e83bfb292ea3f19c3d4258a20f927c87188e
E4 bandgap_reference_de 7576×1077622728, 43104 6.6 MBeeaf0f8ecff9764f06a32b731a0da67e368113723896fccb2aa0ee12861c9bb8
E5 renml 7576×1077630304, 43104 11.5 MB06e65c4a2e3a7efccdc983914560e7884782464fb25be7df1bf65f62cc75636e
E6 io_expander 7576×1077637880, 43104 6.7 MB97df9b1c469533c07ada2a42345ad6bb54c20ed48da9056288fdeb1eb8bcb721
E7 digital_pll 7976×1077645456, 43104 6.8 MB3b521b4419c47fd9fc8c89952cff23b26a69e3d141903138ff6eebb6e49e5e41
F1 zero_to_asic 7576×111760, 53880 8.2 MBb5d06228dffb9f4bd30e2ac220f96fff4a3e0854c097d4afd5d6da71c4a94a98
F2 caravel_fpu 7576×111767576, 53880 7.3 MBf4cfe01cf723ce42ba79618224fb3d30c27b56ee722c83721af929132b1d6110
F3 BrqRV_EB1 7576×1117615152, 53880 12.6 MB1f48102ebdb70e72e4fbc0b2b851ba2c7c4fc01e8a4cfe8e5d1b1cf8a656f55b
F4 hilas 7576×1117622728, 53880 6.6 MBb35db4ff06ea91121ee28eb318210a0153a3797a261941fb826b41ad23af5d52
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 6.5 MB8c6a4e448a8e5cb87f824ef4990a99b49bcfd6cc11528b8847c7b62e77fc8e92
F6 sha1_engine 7576×1117637880, 53880 7.6 MBe5d39a0eb38701e74d5edcc03c5a4150ffca70845b23bab208e3aabb2a5270b0
F7 caravan_openfasoc 7976×1117645456, 53880 8.4 MB8bd13b7fc791f057894743a43c614889fc88fb8121f2423ba7ad232683b08730

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 P1M on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask P1M

Mask layer
P1M — Poly 1
Process step
61 — Poly mask
Drawn layer
66:20
Layer on the mask-layer GDS
28:0
Fill layer
28:28
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.