mpw-002 / MM4 Metal 4

the MM4 shapes of all of mpw-002

40 of 40 dies rendered.

206,274,870 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 (333.8 MB in total, largest 13.2 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_MM4_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_MM4_reticle.png
the stitched reticle, full resolution
162.6 MB 176eb9c8fde54fd981ff07e16c81f5fd5ca48a50190f34b103963c51ac2e7fd285a921dc 1ac2817e484187a929c36b22e2ebd4e3868cac82d8c77bdeda9c6c0860d5c350
mpw-002_MM4_reticle_overview.png
the same image scaled to fit 4096 px
5.9 MB ee55ada164119da722345f9f874b692dcdf196e2c16f8dda9c25affbfed731ee0b128d84 0f5184e774a0c46bd1bc38406fa531b2975ca5265bc60a3cfce0a344021a74f2
mpw-002_MM4_tiles.json
where every tile belongs, and its checksum
13.3 kB 9c287dbf249ca849f1c75842b78d5130ea630b2164df8d30c230fdee9e103ddce055398f 62afb0aed2f761fbf588a76a2054b562616cba8ff2d73463ff102e9520b6a6e1
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 9.7 MB13aa6df728078c6528a293f816c00f1c2c3e3dfc1903af5511e2e3ef1cdaa75e
A2 wishbone_can 7576×107767576, 0 9.3 MBa073731abc9c6a44da52544dd518732cb961c2226adc7ad0ed771513b6aad952
A3 Analog-Neural-Network 7576×1077615152, 0 3.3 MB933aed76bd91ffcad77af1074d91605980818c920368b189c1f5daa3d5759727
A4 caravel_dsp2 7576×1077622728, 0 9.7 MB68f2fe830864afba86eb161fadabbaad5725c2c650805381fdfa8f90277fda13
A5 eFPGA_v3_caravel 7576×1077630304, 0 10.4 MB0bc15919d5df5378cd868c2e3f49fde60df96b22713743ee11cb0b315bf496fe
A6 opencryo_testchip 7576×1077637880, 0 3.3 MB5a8ce1039e27ebe4151a2e3d519b4d4aefeb859e01ecc3697fc2d4bed00050d3
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 8.6 MB79517962cdd876f0d5cd8aa84dbccb7d350eab87ce97c766e611b39166de1858
B2 lexicon 7576×107767576, 10776 9.4 MB8ef891a641c2e192869c35c935db90ea73ea5760f812bc52d70b2ee94d07757f
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 9.9 MB1104655f23ff874f141f430ebcc55fbff54d67158d7f0a7373a587be129be984
B4 yonga-lz4_decoder 7576×1077622728, 10776 9.7 MBf273a071e32cfa3755588e5dd085767c416b4ba339ddae23eec1fe0f5a6957d5
B5 Potentiometric_Digit 7576×1077630304, 10776 3.6 MBd51d51fcd98c34793469c8e2b5ed3a938b42c41669a58eaf39c0abe0de5d3855
B6 fuserisc_ver2 7576×1077637880, 10776 9.9 MBa6c02c4fd0b2cb4ec752b692a34fc3a999bd63af3a14a84d90014d1106c59139
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 3.8 MBd7198b4889540a3d77165e4c49af197c0af155505f53eb93f652ed7df1d9d38c
C1 Comparator_3v3 7576×107760, 21552 3.3 MBed574eb57099291a9c284cf7c249ad55bd2ec361b87b03c1e48ac971ef7ad287
C2 caravel_multi_encode 7576×107767576, 21552 9.7 MB22644477cbc2fcc74efebfd61398d8875c8bee4a8527badef8065c386c5a6b47
C3 kasirgalabs_uart 7576×1077615152, 21552 9.6 MB53a780bbbae5d2ad4bc11719d06169c05f5494f2c14595554fed63afc139916c
C4 Automatically_genera 7576×1077622728, 21552 9.6 MB3ccf017ba028efcb5c2f0f4155433634eef2d223279a4b14a1b8f55987289991
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 11.3 MBffdf9c7391b2cc38126b9daae333f99e4014b15a1b6c5b3c24adef532bf65b3a
C7 caravel_azadi_soc 7976×1077645456, 21552 10.0 MBa98ce597e3d4b81f85f0d810bd85d75ed07028f340647be9c515da4a6092a67e
D1 caravel_dsc 7576×107760, 32328 9.7 MB6f0bd8b9e6dc9a474a3768f4ff3dcca889906b56cd74f61dc7c1b0b4832a6644
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 13.2 MB8ec82a99bf0da9d3e23f387e6f63831827c303fa86284aec35b1395b18dc3726
D3 caravel_user_project 7576×1077615152, 32328 9.6 MB53a780bbbae5d2ad4bc11719d06169c05f5494f2c14595554fed63afc139916c
D4 General_Purpose_Band 7576×1077622728, 32328 9.5 MB99a0ee58c5e44ac64067df30d46d02c4d18debb9de29ce56177169ca3bd9c571
D5 zero_to_asic 7576×1077630304, 32328 11.2 MBaa411f1d64d47c135d79f6c5e4913107c8825e86b0d4bcb1bbb7f03edf968c11
D6 fast_gcd_for_large_i 7576×1077637880, 32328 11.7 MBe4f2d8cadee6029ee927dbaf9ef3ba114fa2769b1fd5b26803e595d4f6c3130c
D7 Columbus 7976×1077645456, 32328 3.4 MB416785231751fa187969d6d4e9f20a8e805b1847d65c7eee29fdca7f8ed96975
E1 Space_shuttle 7576×107760, 43104 9.9 MBca1bad94b09d71b1d307ef4c4d75c86df2e846a1ca2d5a0125f9f2fa1d98b4ef
E2 subservient 7576×107767576, 43104 9.6 MB97a2b082e08220816de5747bb889100bf943ff77bbaede6f26617ab69db18f6c
E3 caravel_analog_fulgor 7576×1077615152, 43104 3.4 MBadd09843377b092459344a8defbeb8fe52376ba586c9a14986753b342f564850
E4 bandgap_reference_de 7576×1077622728, 43104 9.5 MBf783adf6d40e11b9aee4940f8e8e8bf034edd8b92223e38571b2d2b2214b6409
E5 renml 7576×1077630304, 43104 10.0 MBcce5e1ee8cc1e3b3b749c7ceb0a1c1896f73c9c25dada5efa749e8f68195f8c5
E6 io_expander 7576×1077637880, 43104 9.7 MBe7b382b0b4917d9ac8219f0b93968740e4cefe70380668e0f00d866de2d17990
E7 digital_pll 7976×1077645456, 43104 3.5 MB5ed1e3b31e7e9b06aba592160926ac37a9caed9684ef0db5bd8d0ab29f4a409b
F1 zero_to_asic 7576×111760, 53880 11.2 MB8f7e24533d7cf7e4948e5662583af60c1e15dc32f094410eb4196d246df052fd
F2 caravel_fpu 7576×111767576, 53880 9.7 MB98fb11a8dfb0a95ae0291f74f80c504b7ef914deaab9db94c6a37763e14d7bd6
F3 BrqRV_EB1 7576×1117615152, 53880 11.8 MB3837be0b54dadc4d9d947eaaa28656ade09cc1979de1157b1a632687f1f76b06
F4 hilas 7576×1117622728, 53880 3.5 MB4d40a369e0a91d133d0497e4abe4a38172bca2bcea57438118c73cd1c35c4ee5
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 3.3 MBe6cc941fdbc238516b29f60f6d4bf400078ab6ec44c79579d788cdd7f19cd4cc
F6 sha1_engine 7576×1117637880, 53880 10.0 MB91dbc83cea9635024e6e60db8ae193c3e3182e395281174c24338536117c0835
F7 caravan_openfasoc 7976×1117645456, 53880 6.5 MBbb1176cd32942b104a8a023c33e1b1aec6ef9a0334d466e3578e0aa8dc667533

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 MM4 on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask MM4

Mask layer
MM4 — Metal 4
Process step
154 — Metal mask4
Drawn layer
71:20
Layer on the mask-layer GDS
51:0
Fill layer
51:28
Also
Metal 4
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.