mpw-002 / MM1 Metal 1

the MM1 shapes of all of mpw-002

40 of 40 dies rendered.

717,719,154 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 (292.3 MB in total, largest 19.9 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_MM1_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_MM1_reticle.png
the stitched reticle, full resolution
150.6 MB 2774cd7c269b2e5bc7a395cc79071905d166b32399e0033cbc33565136abb981bc4848de 1d4264b1f7d8de084686f4a6da270e2064f87521bb85f6c24e86c31086b142c5
mpw-002_MM1_reticle_overview.png
the same image scaled to fit 4096 px
4.9 MB 77487d70c7d2ee840571f5bf1a71f30079025528a455f3e4c69765b7effdfb9398acb650 731057751b9563eced038719376c4e66b4756f6230a999d755c0821b3f9d4716
mpw-002_MM1_tiles.json
where every tile belongs, and its checksum
13.3 kB 8b5dbefaa65269d45a3b5d2e1e703e40624d82a285e67b77b28c0fe0396d9d4d5e52bc99 5cd03d62c2ece14a1e93e64e4a1f9c3aeb6d00d5c79cc575a247d30cd7c17021
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 6.6 MB587b46e41a4bb4c25c3fbbe4466c1e43ae88f97469a4a61213b64fc8cdb4f5b1
A2 wishbone_can 7576×107767576, 0 6.5 MB3f5797446042f6fe96f22a91b2b29a8b8c0c330a35b27bcfdd1f20274c8675b9
A3 Analog-Neural-Network 7576×1077615152, 0 4.7 MB19278348405361116fc9007e4aac723b6c12fa7bc9025e6f6ab6912d193c23bc
A4 caravel_dsp2 7576×1077622728, 0 5.9 MBe60ca8e85fb651965cc14398a8e72609b092a94e2671278bcfd8d8c088ece209
A5 eFPGA_v3_caravel 7576×1077630304, 0 14.4 MBcb859d9bd31aa655d4bea909cd3b77d662d3ef23383570874e4ab2f085c60710
A6 opencryo_testchip 7576×1077637880, 0 4.8 MB5d48eb9e07483a5e4ef65aade82d8fc678f083bacf061b73b5e23faf90ecf7d5
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 7.8 MBc1d2b63bb699a0a165c0a25fafb98d09bd577c93b3905e8a7d5140107ca634cc
B2 lexicon 7576×107767576, 10776 9.1 MB7476ee6c896e17071002778f950451cdd3ad2d1b7d80955f41af06b122c4238c
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 6.9 MB0d7f05f3e8489599156c4d0ae395702223e192310bd6e791e069d0dd95df942c
B4 yonga-lz4_decoder 7576×1077622728, 10776 5.6 MB40046bd065853379efb2ae40ad281fea8a034b20055c2815cab4e586dd4d43b2
B5 Potentiometric_Digit 7576×1077630304, 10776 5.1 MBc46e0dcd547cfb02d64060deab646ac8cf856afe1b68ff91fbd705182a400a55
B6 fuserisc_ver2 7576×1077637880, 10776 13.7 MB48e187711bc733330283e8f05e0cc340af2a8aefa9c3fe1f166648532539d104
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 7.9 MB2597316cb900804309f405b4cac6c5c256611c5725c0cb93df5f5bb493e5bfff
C1 Comparator_3v3 7576×107760, 21552 4.7 MB7cd819919fa6b1f9bf966d427a5ccf54768258313e23f1876e497411e056ccd9
C2 caravel_multi_encode 7576×107767576, 21552 5.5 MBf1ba790c22473d074f83012fafeaf3785024f8869c10150359344e09da8b9606
C3 kasirgalabs_uart 7576×1077615152, 21552 5.2 MB66dbea344aa4faed7484c12752802740e971d69095105236d87d94be85fc0ad9
C4 Automatically_genera 7576×1077622728, 21552 5.2 MBc0d8b7db1bbc7cc0a843a4159cb255b0157e83e3ca2c13f5fa5f4913fdc5267a
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 12.0 MB7a3dd24972265243a5f1a768828689961380ad94527e72e151f484124242c2ca
C7 caravel_azadi_soc 7976×1077645456, 21552 7.9 MB6b05e29f130090dd43e0ea06c0a206232e055f64d463a4f1077c1ca09f3b20fd
D1 caravel_dsc 7576×107760, 32328 6.0 MBfbc35b8e69be0936f2107867518e9523b746f84978ab46cb0600e073e7a87dfd
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 11.3 MBb075d9f5dcd7e979c569dc5421756d4e0b7f7894e809cc0e77aa51a2e40fd144
D3 caravel_user_project 7576×1077615152, 32328 5.2 MB2df9d9b664c1567f999eb0c54b4accc73c5005e5d6e156c999540cddcfa4127c
D4 General_Purpose_Band 7576×1077622728, 32328 5.1 MB931b306cbf66f61ef00108f046d0ca22d83e38197c817c6be4376f408b20da63
D5 zero_to_asic 7576×1077630304, 32328 6.9 MB5d03f93d99427891735708ec98c0511c074d18452dff1a58876f8f2ad561a918
D6 fast_gcd_for_large_i 7576×1077637880, 32328 19.9 MB29653201ff111bd2bd1eddb5998cdc8bbdc48446843f479a0edb43298e8cbf6c
D7 Columbus 7976×1077645456, 32328 4.8 MB078b8bc3f2e6c1b56c97f32c2184dc291670f8377fb3c7d6f4501d38ef4a54a3
E1 Space_shuttle 7576×107760, 43104 8.0 MB4ce74a964dd2549adf24c4a3e641eba26f75e507dfb897bfc5fddba153496a1a
E2 subservient 7576×107767576, 43104 6.0 MB2dfef03531d99967ab6b327fecbbcdb5583b90ced288493507a212fff6d95c1c
E3 caravel_analog_fulgor 7576×1077615152, 43104 4.8 MBc847653b78570095f4f0915638cf15bb85ee290d7933397ace5e6503d83c979f
E4 bandgap_reference_de 7576×1077622728, 43104 5.1 MBa53371449f21318a66434737700abf1ee153ae09869e2990f90a9d0373852ee5
E5 renml 7576×1077630304, 43104 10.4 MB4ff6630b6d13a58fec19e79aa1a251117d1a82ee0012d7d884b9f9a35cb7b377
E6 io_expander 7576×1077637880, 43104 5.2 MBd47d93774e7f3e3dfa903b0c8ec0820db7ab7403c8701f44dc460903ada2bd53
E7 digital_pll 7976×1077645456, 43104 4.8 MB49ed162b90861d673c390f2a41bd49c2efeb5afcd83ccae81c2e1f71aecf0305
F1 zero_to_asic 7576×111760, 53880 6.9 MB6f31ae010eabc2cffb037971896af3e1deecf751455e5ce0d39e28f6b8c57e61
F2 caravel_fpu 7576×111767576, 53880 5.7 MBcb857b03bf12d7c19cc5f1915330462a4daaae915e00ccca0232d25f3cbec223
F3 BrqRV_EB1 7576×1117615152, 53880 12.9 MB125387b1547b062878a505df4630399d8ff5ef7ded2463eba5a149d81e875245
F4 hilas 7576×1117622728, 53880 4.8 MB01520f3ab8e3d12ca6eed8828549b35d9547e69c2944dfa8f5e73e7a6aa44081
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 4.7 MB295f7c84379be52799ea7691fda6db1f591b39ee4bf5736faf6b4ffec1f873a8
F6 sha1_engine 7576×1117637880, 53880 6.1 MB47a02ccf00c46ebb4cb25ecdbc2dfa2a7158d0bc01129543295797a57a1219ad
F7 caravan_openfasoc 7976×1117645456, 53880 7.9 MB0f910107febe75052230bc9f8c23c0113964ca96c51273b9cf9fb76d253ae4f8

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 MM1 on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask MM1

Mask layer
MM1 — Metal 1
Process step
113 — Metal1 mask
Drawn layer
68:20
Layer on the mask-layer GDS
36:0
Fill layer
36:28
Also
Metal 1
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.