mpw-005 / MM5 Metal 5

the MM5 shapes of all of mpw-005

40 of 40 dies rendered.

9,910,291 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 (14.1 MB in total, largest 535.5 kB), 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_MM5_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_MM5_reticle.png
the stitched reticle, full resolution
8.7 MB ea9d670650726a5a7454d884f06858192ca38319a2bdb6204f76fab16586343c229fe0ff 18a4a91ca6206be58fb7225a68be66a3e9b2ee85862b5e01c7c94c817054a6e5
mpw-005_MM5_reticle_overview.png
the same image scaled to fit 4096 px
4.3 MB b6cdee4aa816df667702c19cd6e85a89bb649c1ab9ff9844b8af02cc802d41eb0518310a 42ac420ec357bc6a187dd7a7de504f10dd6b9724c5fbca978f35626a8df8783d
mpw-005_MM5_tiles.json
where every tile belongs, and its checksum
13.5 kB 8d01e9a183dec14c05bb3af7a1280b6d8b937a5d7f86d1a8858821d81558a36c64777a06 932fb69cf8cfd8b9c5e216d375b0788ac74d39f81d4889d598db78f2bf18458e
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, 0 352.1 kB4b07954ea30fd49965c7a8afe46f8156d259507875b9acaf9a1ac0d62ed9287c
A2 mpw5_cache 7576×107767576, 0 327.8 kBb7661d561a80d0eaa607bef389f564734841d6ea34d7b84a840a7b92dcd1914f
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 327.9 kB5ab2bde7d677c54c4402e9fc106d9f9039349a6186cc02c91eaf231e52a41a5a
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 327.8 kB4891127a94024868d22705dd766a16f7fad6e077b77390b656a29f89553cb8be
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 327.8 kBa6ad28d780ebbd96b31d583c7e31230f95fc1c7e9e21d142ed68ddfd57c7602e
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 327.9 kB3224858b03b950ad8a97b0ba5d4102bc3e62a64568c81bcec57df8661ae90670
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 327.8 kB8dbe73abbaf8c036060e83ccbd0b984eca851d026a701badc64d2139dd299f62
B2 microwatt_mpw5 7576×107767576, 10776 335.6 kB8fe5d2bbc264b3b18e5786eaa69c952f626ce1b4c9e530880f93492d68bc0834
B3 armleo_gpio_mpw5 7576×1077615152, 10776 347.6 kB6a96de1b043abf4ff6dbca8b4a35bfe01aa150b6a4e785b001fe432c1d53cc0e
B4 fabulous_efpga 7576×1077622728, 10776 327.7 kB46af920f627aefcc33766b53b14ce9e431c3e929d4741403e44880aab31a6994
B5 i2c_controller 7576×1077630304, 10776 327.7 kB6b7f31d2431b9cdc10e7ccf31bd3a1fc0c7f9a50ce654b5f496fea1ddfef7435
B6 lbist-mbist 7576×1077637880, 10776 339.8 kB5358c3b0b141f034897991fac0f7261da91db59422dcaebec31af459c374567a
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 342.6 kB238440882719517e5f3eb21c8e5b9494fbb0bdfbd288f0a79345e7f7a736250e
C1 riscduino-qcore__q0_ 7576×107760, 21552 338.6 kBe99e822a935f864d90f9d1627d752cc2d404c10e6e0f04833cf0e9b6c917109b
C2 high_speed_vco 7576×107767576, 21552 351.6 kB56d64a6cb93cea919900adc1ac0dafb86ef0d051a2efbf1b10b3a1fefeff534a
C3 openfasoc-cryo-gen 7576×1077615152, 21552 497.4 kB9d66b5e583c41ae3657ee09d3bb2a73905a07e418a2631b4dcbc59aa9423154b
C4 microwave_signal_generator 7576×1077622728, 21552 370.7 kBa9780ecd6842b8f56e1d82a00df2c15fe4c0e04030f37059edeedac832778515
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 327.9 kBd4ed621df767b269f9aedbb02c422ac71255a11bda288f27c744c4506b828048
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 359.1 kB85ec00828813a36af153f60d1b03c5a4b091aae26dfa923f440655c1a4361140
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 370.5 kB24b6d8682687371cc7f6bc493d08fcdc33532aaac64cee81473c33edb3013f4f
D2 azadi_iii 7576×107767576, 32328 327.9 kB897131b9ef30f04dad14cf90427ecafd57adffa06d8ec89f6dd9826fca85be7d
D3 tia_for_physic_experiments 7576×1077615152, 32328 366.5 kBc64e7ecee284431d27aea626adb28a514fb6c61eb561ebb9def01029e72ce060
D4 qf105 7576×1077622728, 32328 327.7 kB06ab7beef751a3743c8d2e35b9cfdeb82a70a8ee6e5cb89a249183952c069dec
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 535.5 kB266ebb13cad0eb26ab8a2fd6443ce95401219c917cf00ba3d812a7249155970a
D6 junga_soc_mpw5 7576×1077637880, 32328 325.7 kB9a0ef4e070dad9aa52553720915c773f0baa127a81e793ea8618388f339fdd9e
D7 nand_flash_mpw-5 7976×1077645456, 32328 331.8 kB68f0e267e8497bbdd849ec0e0ade7e1702beee18d8654405c26a22d66d744d21
E1 kasirga-c0-mpw-5 7576×107760, 43104 327.7 kB7db41c71180c90df3c36807f6325095d41370f097c95f20c5ba80f1a5af5f0a6
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 327.9 kB660b775f546050612f7cec034640eb7a533cb184666dd68c23907743459228b3
E3 wirelessenergyharvesting 7576×1077615152, 43104 351.7 kBaf2949b8581a461a02064fdb4aea92b577e71ce913949efbd3fcc803cc6df3b4
E4 caravel_raster_engine 7576×1077622728, 43104 327.8 kB0de1e337599433cd43d2ecb580dd68fb7f71d2734ebcd07c96e2165887a53dba
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 351.6 kBb7fea49169531d2d034334726a793343bd88392b09055e609797fb28eee3a2bc
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 327.8 kB696126f66033f1c116447c07df39faef4665700e0e9383bd76ba9503885cafe5
E7 alu 7976×1077645456, 43104 331.9 kBee5bcb60ea488bd02cdfd6764966db152056a8717b087c7b8a7ee3ae9a4fc5a2
F1 4ft4 7576×111760, 53880 331.8 kB14a2fd2ab2b8a95443877622cfab4192aeacf65b5dccfc32c713fa337811a31c
F2 rng_chaos 7576×111767576, 53880 331.8 kB0325950cb38b715a945058cda939400b6a26ac4c1830b5172afac1330dac7f2b
F3 uetrv-ecore 7576×1117615152, 53880 331.8 kB38b924484d070fb8a0b9811b5c75306d6960262b0654278fcb8d59ef0355c9a3
F4 systolic_array 7576×1117622728, 53880 331.7 kBa1999f6ce55c9d9caee943862e56965000ac714098454b7445282e7d8c812877
F5 reram_test 7576×1117630304, 53880 355.6 kB8cfaccb9ac9316d287d88b96a784d8cab3ae08b5f146e662e1d5a00694fb3053
F6 secure_memory 7576×1117637880, 53880 331.9 kBc53ed724685bb7aa507b8d2db7205cefce1875b033c4b60a06105dcf38d3ef02
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 336.0 kB0b49a831515b23b7dd1fd9603a0c82e1065a5878c1c6f3520ea6774b268169a1

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 MM5 on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask MM5

Mask layer
MM5 — Metal 5
Process step
162 — Metal5 mask
Drawn layer
72:20
Layer on the mask-layer GDS
59:0
Fill layer
59:28
Also
Metal 5
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.