mpw-002 / LI1M Local Intrcnct 1

the LI1M shapes of all of mpw-002

40 of 40 dies rendered.

235,749,220 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 (281.4 MB in total, largest 22.1 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_LI1M_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_LI1M_reticle.png
the stitched reticle, full resolution
171.3 MB 633f8f4f62f9ee672237dc56185747649df1f250bcc22150cefa132b793fc95adc3c0049 d6f84a6df833fb0c3ddcb3b341043d5bd1acda2685abb89aee83f0f7519b7dcb
mpw-002_LI1M_reticle_overview.png
the same image scaled to fit 4096 px
5.1 MB 3233a046dedc4ee2b5ac679509637a1a64a69ad44637591d760092e74b63e7040944108f 2aa00f055356325e73283963463eb156d1874ed6801eb73a45cb06ec267555bc
mpw-002_LI1M_tiles.json
where every tile belongs, and its checksum
13.3 kB 1f1c9b52341697fce42198524b66864cd8ff16194bd16a262b1332f5c0f2c7da597f13b9 3262b469fc0351d9128f2223115af8fd6e7da9917a361f61a924da32b0848b02
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 6.4 MB46566649d71ccbdaf82e23785256f853d7943bd35c25e68a1b23425ae2b57ee6
A2 wishbone_can 7576×107767576, 0 6.5 MB91256b13198ac7c7317b0c30b5584e677472fe1b1600a835818a62ebf008d62d
A3 Analog-Neural-Network 7576×1077615152, 0 4.3 MB74e2a922df557d7d781aed1e2970282ae8e76eb654f7e22199d546064346a3dc
A4 caravel_dsp2 7576×1077622728, 0 5.5 MBca7ee81438610b9a3a649b6d6415333f6bc2847480f796ef0399f6ef7abb04c3
A5 eFPGA_v3_caravel 7576×1077630304, 0 14.8 MBeea8d800e32ca3e0a0bd7a9862c7b7f1ea7b8a5fddcd84d82f76df9c3d8ac8f5
A6 opencryo_testchip 7576×1077637880, 0 4.3 MBeae28d65fef4bd8f2cfd7feaf46f2dc0f5f65d82ee3c0e8d678f9f2e06288a7c
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 8.2 MBa4cbfa35b2c452934ad55b9780b46f8546d1243d50bb75aa112f65bcc1785701
B2 lexicon 7576×107767576, 10776 8.5 MB9eb4d468e902273400de2627b2007bd3ee6c52f6b04a79787995d2be3d2badd8
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 7.4 MBc77389d69e1c475ee1ad38ba65396296385e1ffce60658db06a14b73030f3fa9
B4 yonga-lz4_decoder 7576×1077622728, 10776 5.2 MB0209f03bb5e0862d57973d958ea1a5977357a6dfa13aa161e369aea3f5bc37bd
B5 Potentiometric_Digit 7576×1077630304, 10776 4.9 MB7927f88663521e4d4905af97ba5ea512286efb8ca759ea845516da19e78b6905
B6 fuserisc_ver2 7576×1077637880, 10776 14.6 MB558d779703c4511344b8decf1ce7e5ee2d109024280bf42ad7118483b5050795
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 4.7 MBfac8fa2de9c32c61fbfafa3cb6f723bc63fe92fcb18a831cbdb387e509ea0f34
C1 Comparator_3v3 7576×107760, 21552 4.3 MBf30b4ba707540614648737ab3d7f06f7ea1798985e94516caa6525607fe2a8ea
C2 caravel_multi_encode 7576×107767576, 21552 5.0 MB4bbd9d224e37f58dcf8eb805fbfd4081aab6f7e923ef43400a96374e41dc0daf
C3 kasirgalabs_uart 7576×1077615152, 21552 4.7 MBa2f6dfdddded98ee9dce555ddff9b9a80797a9272e5714116ecfd1b9245611ff
C4 Automatically_genera 7576×1077622728, 21552 4.5 MBe05c0753133fa8026ba18eef16442b93e77b5b1774295076c00fe850dcaae79b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 12.5 MBf33216013177fd64a9df1e4875d25de9848461a0c4920634db70b217595f44e9
C7 caravel_azadi_soc 7976×1077645456, 21552 8.9 MB0e5cde1e29b69cc1416795a44aa31c2091635c632d95eb9f5b610df101d3649c
D1 caravel_dsc 7576×107760, 32328 5.7 MB70afa020b236e908e745acc4d3ac85ea2fe71bd89c952659fdb11bfda4c9c0e4
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 9.4 MB779df801ff859693b02c5e5bf98d2de01057080ad55c6d5d2388a78f5b489343
D3 caravel_user_project 7576×1077615152, 32328 4.7 MB95d625a1679c9607bd2ffe2a08c9d4a5a148448af534f26d3712863a0ef3feb3
D4 General_Purpose_Band 7576×1077622728, 32328 4.4 MBe85b2d721eebf6d9e91f947e5a920b2f39be51b11164228450ddadd6f62a60b8
D5 zero_to_asic 7576×1077630304, 32328 6.4 MB0d1b6eebf094119d5a7e3b67628eac89c87e92cea0a4e22f648181cf3ca6ec16
D6 fast_gcd_for_large_i 7576×1077637880, 32328 22.1 MBa72340115767a6f4fcbb8e942376abf7c57bc2623d2ce238ef9479de4248e307
D7 Columbus 7976×1077645456, 32328 4.4 MBd4e34acadbca700a50aab728dc626d985e7dc1306f68cbdaf4159a3ec3c16171
E1 Space_shuttle 7576×107760, 43104 7.8 MBfcdb3cc8afddb7c20e729190c9f33ca980f253c2283c9db1b7bb8454d88563b0
E2 subservient 7576×107767576, 43104 5.3 MBdf6f67407c18924e5920278753a78cb645ae353c447fc973be4075a95bbb71bf
E3 caravel_analog_fulgor 7576×1077615152, 43104 4.4 MBc70b4debc6951e573ecc315e1ae091271ce93f57aba1092ca7909d7c5d86585e
E4 bandgap_reference_de 7576×1077622728, 43104 4.4 MBb0b9b07f1b72a124c8939ed5959a31e800d140784a5eab7e4d5e2edde58b90ac
E5 renml 7576×1077630304, 43104 10.7 MBfe7e6528617182ae63ed3e6cf46e655fca006da5eaa87f04858996aab1a6d5d3
E6 io_expander 7576×1077637880, 43104 4.5 MBd8649b83e3ce4b06c1059f85b22b65045044ad919ed266a6ab74820aca02f9d1
E7 digital_pll 7976×1077645456, 43104 4.5 MBfeb9610d522b0ec2973f2f56d690bcb8999176330844dd5d0790029cc124ecf1
F1 zero_to_asic 7576×111760, 53880 6.4 MB196dfbe707b4e614af663c2eb01a4bed2fb57be0eb80ac8a09601a447b5d0c85
F2 caravel_fpu 7576×111767576, 53880 5.3 MBc597659896b3a3d7272bcf3b48db9df74ae84ee5eb73537b8110d1f5af49f0b2
F3 BrqRV_EB1 7576×1117615152, 53880 13.6 MB2308799c3f54a0f112eec93cd5e814ec357ade65f940129d8cf7885e1ed00939
F4 hilas 7576×1117622728, 53880 4.4 MBb0e3ac09f54d1b080f2d62b3f7f09a4717626d83b27d74abf198fa1292912249
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 4.3 MB9e68eb2d11a5a844595c800c4ef780039873f387f8b512fa890c2417b26ac155
F6 sha1_engine 7576×1117637880, 53880 5.6 MBbd08598db0cfe9ceae8afeba9de0c95bf17a174431c567f641c8b6ba812f5085
F7 caravan_openfasoc 7976×1117645456, 53880 7.5 MB4325e1d3af228baee1e59e1995b4a008495787a98ff102ee9c4a93ad781c3e81

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 LI1M on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask LI1M

Mask layer
LI1M — Local Intrcnct 1
Process step
102 — Local interconnect 1 mask
Drawn layer
67:20
Layer on the mask-layer GDS
56:0
Fill layer
56:28
Also
LI (Metal 0)
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.