mpw-005 / LI1M Local Intrcnct 1

the LI1M shapes of all of mpw-005

40 of 40 dies rendered.

203,765,710 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 (247.4 MB in total, largest 13.1 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-005_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-005_LI1M_reticle.png
the stitched reticle, full resolution
147.1 MB b9cbfc8949aa619d656278b4ac4b699d3de0bf31cbd36a33db308a273d4e2c476c0a2be7 6231e449875f8491138935457ef865d0c132beb5ce3f62e731acfd37d116385d
mpw-005_LI1M_reticle_overview.png
the same image scaled to fit 4096 px
4.9 MB 9116917d99ce392880473d6ade83bd724abb63e0a4ca4aec30256e3665c958adaf8609c2 2b797c7e5ddbfe7e0f7c5c8f30a34b548bc24a87dd522ea9c59d290d7bf7b9e8
mpw-005_LI1M_tiles.json
where every tile belongs, and its checksum
13.6 kB 734bd0d457db915d8273a9374cbcd39fb4d7aa6584f7933def8b684094d684ffc690ef2e be21bd79112a8ee867dd8b3e19085e441e87119fb9f1c6465909f649b82e2970
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 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 4.3 MB97e8e9feec1aa8ba4c04632f5a4992f40a40c214c648cb45aee15d91685b1bc1
A2 mpw5_cache 7576×107767576, 0 4.7 MB822cb404b5454b422aa2276e69fe91228eab57a9a3d75c858f14930b0c213194
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 7.6 MB186919b9768f21dbab88bdb2a54b4ba7b27bcef047bdee97b9e46db2fbbbc60c
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 5.9 MB9e91bc17c16988df2a9c08e67f9ec8996d7d2d3bffdd4af35f6f90084d2bb038
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 5.7 MB3b553e9ef5705e6412cb82269e66565fa23312f0cd618a8ba6dc6159754fbdbf
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 7.1 MBb94f83b0837a68b1d2e8df7c4db5770d79baf681a5ea2b67c413675f94d24814
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 4.8 MBdcba3c45cc59a4f7579bee0c015139fd2b7cce3aeee68d93233f3cc758c7bb54
B2 microwatt_mpw5 7576×107767576, 10776 13.1 MBbf45982b4936a180ff7ccaee31ff6229a671fd647b0658ba66f1549f23beca01
B3 armleo_gpio_mpw5 7576×1077615152, 10776 5.8 MB4da9f03bde9dcad0e6695b517e197992c133c22e90484e0c9b456d4da9dcfacd
B4 fabulous_efpga 7576×1077622728, 10776 12.1 MBcfef85961298e4cdf9859ab9c45b2613f2a95b1fc8660104674841e9cb1a966f
B5 i2c_controller 7576×1077630304, 10776 4.6 MB0977adaaab3cb602cf96aa4af03b87b55562eb7e910997dfddb531ea484bbe1e
B6 lbist-mbist 7576×1077637880, 10776 7.2 MB9430fc1377845e7685768bfee438aa7002ae287a7663b88b90a6800ac4f96aa9
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 9.6 MB2b52f1a3003ab1e0d6ef62422ddb5b71ac8eb9bb4596a09b1f1342fc1a6f246e
C1 riscduino-qcore__q0_ 7576×107760, 21552 9.6 MB42a28ea4e76a10d81acc4bc11ccd28d40c707a87858b9cfd4fedd86beda29c43
C2 high_speed_vco 7576×107767576, 21552 4.3 MB1bb2d6827e9d9c21656d623b3e1eb05665af2c5bf3a2403e08710470204d16db
C3 openfasoc-cryo-gen 7576×1077615152, 21552 5.1 MBcf59263bc8a8c5555e255a98affad5fe04dab661ee06f92c73c57a05da36c779
C4 microwave_signal_generator 7576×1077622728, 21552 4.8 MB601bd2a683234f25d62fd7cf3b9d929080f790394c37cad96f77f822c7b48d3b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 6.9 MB50251797e9989b3ab7d40d2ad00f98ae42723f217778473914e8329d978aab93
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 4.3 MB6e0d230e8b9af5fc9e52db2d5cad1e25a0a501ce7dc29545d8d14b3da824ba38
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 4.3 MBd9c6647ac9e86cfc1d7365543279073aa419fe401fb9a55fd7c4f42f4e4ee158
D2 azadi_iii 7576×107767576, 32328 8.5 MBa67a2152ffebd98ee171688894c8287f286bd8ee91e853a85fec41ee5fe5f2b6
D3 tia_for_physic_experiments 7576×1077615152, 32328 4.4 MB370ae76035a74ac8030eb925b4a7222569906078290ddd9bc75bd23e4c3901d6
D4 qf105 7576×1077622728, 32328 8.4 MB23be249e35e657647384a98a6a6b009e25c9f46123611ac0df7176e0a4e0d990
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 6.3 MBc4804a5f872b49c9085f43fe33b5d0bcbb03d48be7b311b5613f824be39fda32
D6 junga_soc_mpw5 7576×1077637880, 32328 6.3 MBba48ede76a6a881a8594bd7c4287a3eec62a5b1ef134dc12ff3443fb56a23b70
D7 nand_flash_mpw-5 7976×1077645456, 32328 4.4 MBb3ad4c39a94bbe0ec77381bf34782188b424b01b768f73cae84edb4d52123b08
E1 kasirga-c0-mpw-5 7576×107760, 43104 6.3 MB4aa09a0d38c6beb5affcb22acee999649029efbb9797ed30cd897b228e78d02f
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 7.8 MBc0d7c3e248ba34525f95f93732e1351e9dc2e81b4096525e705506f19b66a375
E3 wirelessenergyharvesting 7576×1077615152, 43104 4.3 MB31a2c0a00ac7bbb76778b5b6a263f6ea8b4b982410544ad59bb0be3ca7aba71e
E4 caravel_raster_engine 7576×1077622728, 43104 9.5 MB0a5357f5dbbc07e5fc8d18f80269a047528983bbaee3ff534562cb5bc5fe3eaa
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 4.3 MB1bc8ac9fe22d003468efa254fafffc31fab6022d56b5eea24dd626dff5e97a7b
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 4.9 MBc345d8bb725e70458a91d4d430c92aee440e6f8ab207fb41fbf6dc34f7a07f16
E7 alu 7976×1077645456, 43104 4.6 MB6b0bef413fea9bdccf4ae5caf297988f9fc3364cd9eeab6f027a515bee910893
F1 4ft4 7576×111760, 53880 5.6 MB75d14148dcef160a8d5c0fe2bcc7fc9c19d56e6f8b325077db5282051d2a29c4
F2 rng_chaos 7576×111767576, 53880 4.6 MB0118bf52d23eb72264d611b06bfec10904ec8a0594853bab962a13c8336b9ab7
F3 uetrv-ecore 7576×1117615152, 53880 6.6 MB992435801d713d20a7840116da718da49a5696a72ae89460e4c7cbb3e5d0c0c9
F4 systolic_array 7576×1117622728, 53880 4.8 MB6f26c0b40a51ce4c994aa696bb4b2b87d9388a1150c703f58e0618a52963d21b
F5 reram_test 7576×1117630304, 53880 4.3 MBfe94f29f1fef7c71617736ebcde81ab51cae886bbc4b9bc67a58b8b5e479a85c
F6 secure_memory 7576×1117637880, 53880 5.0 MBf4f9ada55f0bf5f82076e920d720a281fb0445de4548fb3b38e3938ae4d9f49f
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 4.7 MBddc65e57e54e86982f4fc2361149fce6da6347edf8dc2c66729e8a226b58d692

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-005

Resolved: reticle 5CS8011AC, run mpw-005, 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-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/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.