mpw-002 / MM3 Metal 3-PLM

the MM3 shapes of all of mpw-002

40 of 40 dies rendered.

227,113,596 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 (217.1 MB in total, largest 10.5 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_MM3_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_MM3_reticle.png
the stitched reticle, full resolution
108.6 MB 71fd33c41aec6dbfca8e48851059e2960ef467854ecd64f99c92ce8b2fb8f97b1b4eb297 ad80749217cb5ec259638c8ade8bd84802f0f959f53692727331dd860555e2af
mpw-002_MM3_reticle_overview.png
the same image scaled to fit 4096 px
5.9 MB 33d7ad0a4728f68df2329fb4faf6e2a0c123442f27f6079a9baee8799c8ea625281781a0 db0a59d53c922e2b97f8bb796f320a60bd48404e0201e6452e4975a20b2f2023
mpw-002_MM3_tiles.json
where every tile belongs, and its checksum
13.3 kB 8f432af486e463955c5f5e6fbf93837b7859c495b2b5f6225363a593dd54797044556915 0a10b2b1ddb85b7146da2ddea13fc9adbb52fdee5ed97abc39ee7869cb656a41
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 5.5 MB284feed5ac8ef274cb6f566d37d67bdf8131899d5896db4edd7748e3541fad54
A2 wishbone_can 7576×107767576, 0 5.6 MB4d3319c068f8176b42ac6b9aeb1e9e02915baa460bce815bdb155f53e0320bd8
A3 Analog-Neural-Network 7576×1077615152, 0 3.0 MBbf5cfc6f3ab578f9ff71bb5b9a1c2c7bb6235b4d7a7dfecb3eaf034c3c7b640f
A4 caravel_dsp2 7576×1077622728, 0 5.4 MB6c808abb19eb21c02c29b4f904ec916d4ce81d411b30bee2f21555dd2e80b79c
A5 eFPGA_v3_caravel 7576×1077630304, 0 9.4 MBd00f17ba444de1783fe51bc446cde0478725dad78c6403887180f78469ea4e0d
A6 opencryo_testchip 7576×1077637880, 0 2.9 MB92fbeaa4c7c6b7c491d12f77676b7cf9852a5db610d9c717481e1ba27f7c6853
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 4.6 MB76180631498f4b3d6b8f18a8dd58e1f82f51141a71296c2dc5028a27b47ffd7b
B2 lexicon 7576×107767576, 10776 5.9 MB6858da759c23bbe9e489d010af8b0c75189bb8aff09c4a678aed381e2899a1e8
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 5.6 MB7b1fbf114be37db9b8222a44b44ca0e4ff7100b6def653e42c559825d67bb621
B4 yonga-lz4_decoder 7576×1077622728, 10776 5.5 MB110097d5ae386588ff122b34697b05d309a1da4f2bf7bb1f2cb788ae13399bcd
B5 Potentiometric_Digit 7576×1077630304, 10776 3.1 MB32c4fc93094e9f46dd77150781b4da59fabd42c5b749818bd27f993e8675b515
B6 fuserisc_ver2 7576×1077637880, 10776 8.7 MB14c761b5b00ffc9a1a94b3f964aecff04a2a380e21aab10a70cb34c9a795871f
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 3.2 MB14df0af4c21ee2ed56f86747b8ef6f59827684135b3cb80ad261217e04bcfb5d
C1 Comparator_3v3 7576×107760, 21552 3.0 MB57ac3f1ee694bc8154d0fae475778717d495c24f1cf4bde56f63dc10d8a52f72
C2 caravel_multi_encode 7576×107767576, 21552 5.4 MB2e52427829dd8f04503f6ab176dd6d2c28062745b491b1b0b5a067962c6c4f75
C3 kasirgalabs_uart 7576×1077615152, 21552 5.4 MBc2e50060eeb9dc64454fa7962b5d2473cc0ce3969828ae306612c84b0a74a0ac
C4 Automatically_genera 7576×1077622728, 21552 5.4 MB085113227df27a0a83d0fb7d8537951f621c862468c607e667f5fe376c541ea9
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 6.9 MB5b3dbdd3d5f0bb630c0b8055bb6692f17e1976cd43f5f6aca4573e354617b974
C7 caravel_azadi_soc 7976×1077645456, 21552 5.8 MBcc9095ba22928a9c65e80e086ca3522db331a45c69239e39771ab5a9320f54cb
D1 caravel_dsc 7576×107760, 32328 5.4 MBc393de074d392be9312b5e0e8c07da0ad0009ce4acdc1fc381cd49c93f93d839
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 9.9 MBdc0bd3f8b5aedaf67f337f3ed977762459ce12adcf04f049242d967ceda17891
D3 caravel_user_project 7576×1077615152, 32328 5.4 MB36071e0853991350a6beaada7b2d4ea26a5c860a5a2b5463645bc09df2773c02
D4 General_Purpose_Band 7576×1077622728, 32328 5.4 MB032bee3452815f1b2f745d982ed73af03973cc78ecd842788aa810f3b92595a5
D5 zero_to_asic 7576×1077630304, 32328 5.6 MB76885f39926a26848a3d7411391617a14eda9b754cfc6aacc6dfb76dd45f4bd4
D6 fast_gcd_for_large_i 7576×1077637880, 32328 10.5 MB45a869fcd2b5f224358a129a4c19a6724bfea254d5cf44da853c57d038b90ab1
D7 Columbus 7976×1077645456, 32328 3.1 MBb6e2181737ef9dfd4e99ea0cff876d2da22208cb17d1bde98badf73ef4a8d4f8
E1 Space_shuttle 7576×107760, 43104 6.1 MB5a9f63272eb807e188cbbc0d007f3ba820b3b7d412f89e3a54255759072ad150
E2 subservient 7576×107767576, 43104 5.6 MB2d508c04cc20a31668b80a5b5a949c5a2a4254ccd378bb830f1ce960a71d5e64
E3 caravel_analog_fulgor 7576×1077615152, 43104 3.3 MBc5517398cde4681a0edd373c4cab678caa79ce7be853b9e1453a569f8f1c9eae
E4 bandgap_reference_de 7576×1077622728, 43104 5.4 MB965a7d40c9f9c03e96eca87a58472dfe85db03bf3ef6a84cafc189bcadf306eb
E5 renml 7576×1077630304, 43104 7.0 MBebff2d2baddf6240f025c9fe604733d5179a89bae3088685b4fe8cd386aec177
E6 io_expander 7576×1077637880, 43104 5.4 MB886a17cfe811a7e435f790a0713d4e46a1e3cea8976ea0342c02d6ed4f7cdbb4
E7 digital_pll 7976×1077645456, 43104 3.1 MBed521352e66dd12c787a52bc3fd0dc35942a3e68305d1a80826f4728cf6846a9
F1 zero_to_asic 7576×111760, 53880 5.6 MB6a1f7e630acf2cbdab75d08ef09b5d00669e1b8db577785a3696485baa4e9df9
F2 caravel_fpu 7576×111767576, 53880 5.4 MB481bc8cee5ef5ece9d4334600d42d718b85c997cd5501ce422553e18f997077c
F3 BrqRV_EB1 7576×1117615152, 53880 7.4 MBda764123bf71fe08d926492a970a7351855f2c99c5f8b283e2a0366ca14164f2
F4 hilas 7576×1117622728, 53880 3.5 MBa4b9dd2a51d1b03dd2da3e93701acd13c249d5fa46572926e8b402ee3b24542f
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 3.0 MBb282f8bcf0d75bca27e9548954d7e7fe7131b88c7fa28b7836b9c8cdda1791f0
F6 sha1_engine 7576×1117637880, 53880 5.7 MBc019d25225363252cf0fa11a4f7bcbc7218017717f1448ab11ff4b8f62d3dfb9
F7 caravan_openfasoc 7976×1117645456, 53880 4.9 MB53ad5d10d23060c148383e93eecac7139514ec9b4303b8275851b9ac593593b1

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 MM3 on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask MM3

Mask layer
MM3 — Metal 3-PLM
Process step
139 — Metal3 mask
Drawn layer
70:20
Layer on the mask-layer GDS
34:0
Fill layer
34:28
Variants
Metal 3-TLM, Metal 3-S8TM, Metal 3-PLM
Also
Metal 3
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.