mpw-005 / MM4 Metal 4

the MM4 shapes of all of mpw-005

40 of 40 dies rendered.

201,505,342 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 (330.4 MB in total, largest 11.3 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_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-005_MM4_reticle.png
the stitched reticle, full resolution
145.6 MB 2d84a2ece7b28dbf2a2f015f9f191aaa46975dd55c029c3586582f6bffd9a7b1c9e17dbd c6a48361c84ec2b668f5c33f96e2ca8ed60409a7e2a14c7dbf49395330faf1bd
mpw-005_MM4_reticle_overview.png
the same image scaled to fit 4096 px
5.8 MB ea2177863e2015d25e3c733992db98c305ff657ba2cb68c406f9602bf47ee20a7f28b6a8 8d1875a2a5e5ad2e6c1070f281c9774312d363616bb552b0f3447c2936a55e0a
mpw-005_MM4_tiles.json
where every tile belongs, and its checksum
13.5 kB 5785873930de894da614e29fe3a30ef92e53b2dae9901b328bfc27a40072bdab4e08014b f53339a2f98eb6229d8c8b6a300b705ac93135c3ecd688ecded1d576846a1860
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 3.3 MBd00e302d5227c1b6ef4b3265328334130921fb04f0b81af5be54da9c97d7f1c6
A2 mpw5_cache 7576×107767576, 0 10.3 MB08de2b09ac8c4fda23896be5d4c123f6765ea29125be742a86a1f66acc34c1f9
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 11.1 MB125a96b738dcf7196046f477fa9acd1573ce6db922d41226b3ec0ef40499f1a8
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 11.2 MBc799c9c67f35f07e9e47aeda56b27a4f10c0b6d74c561a6de19ef640b46de9dd
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 11.3 MB8797d8a7729a2ebf1b4f6c7706618d67dd6f729ad5f89eb3b39eabdedae52eec
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 11.3 MB3478b05586463cdb65816e8cbb1c9805ad86e7e22dfc47192bd3f40365831fb1
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 10.1 MB619031650a5d8111646321aa9552f13a5ca09908371f58f89f558de9a10bf5f0
B2 microwatt_mpw5 7576×107767576, 10776 11.1 MB9a9018e15046f430e6248093c55972f393855885262f2870611b3db7eed5dca0
B3 armleo_gpio_mpw5 7576×1077615152, 10776 5.2 MBbbf6984123f5276e314dd15dc83b3d570e4b61c355906599c2389d3073784c6d
B4 fabulous_efpga 7576×1077622728, 10776 8.8 MB2a0c8449d8365488166f41941fe6906c4bb44563991c4f41eab73134df2cd80d
B5 i2c_controller 7576×1077630304, 10776 10.3 MB929732b2538729086c5d8abd7fd4f58c87c154224b75ef0df7ada861760eca6e
B6 lbist-mbist 7576×1077637880, 10776 8.3 MB0dbcaebfa2957a261276272054c9fff3f9b81e1391477509302cf21c51a59403
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 9.2 MB32dadf4312e90a0b5ed9f0e068b1947984ee2b72941942d06c01b74ee8b943e7
C1 riscduino-qcore__q0_ 7576×107760, 21552 9.1 MB01bff33669d6056c3be1041ba02f314ed8850dcfbbe9ab55ec2b0e7c6e4d990a
C2 high_speed_vco 7576×107767576, 21552 3.3 MB5dca8d0a3f9e8e837732fddd8ac0247b2d891810d4cf57a012f460b3f8d3467d
C3 openfasoc-cryo-gen 7576×1077615152, 21552 5.3 MB3d59b6061632fae1da3ac6d482d58ea8237a9c93f97a74d54c26f64811718383
C4 microwave_signal_generator 7576×1077622728, 21552 3.5 MBbecf98d0795b5c5ff1ad9d5a006b9827a8715a2d16e50f14cc9c486bb9070fb4
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 9.1 MB9cd24424f2e7cbb4d329657035f04e9485688bfd7d769cd93486526aabab0e26
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 3.3 MBb5ae4031e98ce21d827223f1d0f9f2a57522c3c2d4b6de3a003987a40259d23e
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 3.4 MBd113801b02c4ea703ddfe31f3e182f0d7fa2fb99d65f7862317f3fa0a92ede77
D2 azadi_iii 7576×107767576, 32328 9.8 MB74356b42b943999fd87591406d8b6eb59d693717e88206e828ab90f2679ee1d9
D3 tia_for_physic_experiments 7576×1077615152, 32328 3.3 MB0c2619f42ffa2fddde5cd35ba39da61e6e274ca9c48be75c358ad0d1a7d87a39
D4 qf105 7576×1077622728, 32328 10.4 MB8de81d3431a218ef36492a5e66468e4157bec41c6d8c5a5b6f05aebb527b0941
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 6.4 MBdd311efa45482fbd59737750d974b7a46a8cf5e23336f9305c420212b8ef3259
D6 junga_soc_mpw5 7576×1077637880, 32328 10.4 MB7e9507728835448cd8c79ec8a3609c6ee3aa4e69136f6e02718f535bd9d88ea9
D7 nand_flash_mpw-5 7976×1077645456, 32328 10.4 MB475364c1854b73d02ccf67d088b3991e4a76999ffb59b35d2865852ac4aff821
E1 kasirga-c0-mpw-5 7576×107760, 43104 10.1 MBad8fc6f77ab13a5b397eacc34ab601dc86c694d305bd26153e2472617fdb1e9f
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 10.1 MB272c87b519b2aa6240e8b7b92524f439d7f8c529d90f891f85be2b4112a16920
E3 wirelessenergyharvesting 7576×1077615152, 43104 3.4 MBcf83808cabcb5cb5053f9c8c58eea57a402371714a822e42fdaa87f07abe31e7
E4 caravel_raster_engine 7576×1077622728, 43104 9.2 MB9a6be4cb740fd9420d53ef6dd763e732d6a3b4ba48695ddadb04aad7b7195dd2
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 3.4 MBff35abc4a0eea395c113154c4efd73c3761a026f013b37c40e5e66e8ea46b900
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 10.0 MB01d517d9979016bec086bfbe1ca3f233cc37e18663808dc7590b2ae5fd522291
E7 alu 7976×1077645456, 43104 10.3 MB27e649b67843cd1de36aa38af945b85b289a60859726d89e8e1c596d3d585e55
F1 4ft4 7576×111760, 53880 10.6 MBe245c5fe75926376ff724e9e34e162f69defe3170074beaeb943b757d9b6f954
F2 rng_chaos 7576×111767576, 53880 10.4 MBa8e38058e83d5b56b0795e0fef9b2bded3e607a8bc29e6e64a3ac0257f1c3941
F3 uetrv-ecore 7576×1117615152, 53880 9.8 MBe84e2a586f4bd9e1adc3c4075ec0747c72c2c72d7b6d1809b1cfd95f03bcb449
F4 systolic_array 7576×1117622728, 53880 10.3 MBc1d2c63247b7260cf64eb4d5675116308c38bafd26600c47c7e9a658766763a1
F5 reram_test 7576×1117630304, 53880 3.3 MB628877874c55d0a4721b338325c27de1cee17051e75b8c90fb824975115375e2
F6 secure_memory 7576×1117637880, 53880 9.9 MB08320986e0a2864426350d9a8ea0c65b181622a4e1a6498e2ca565fe29d8fa32
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 10.3 MBfe79af586849b9c53d498e402b3ccfea9b70433ef3e0c0685377601bce3dc49c

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-005

Resolved: reticle 5CS8011AC, run mpw-005, 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-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.