mpw-005 / MM1 Metal 1

the MM1 shapes of all of mpw-005

40 of 40 dies rendered.

667,890,484 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 (261.3 MB in total, largest 12.5 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_MM1_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_MM1_reticle.png
the stitched reticle, full resolution
125.9 MB 147b660da1f4607754a43fed59c4b7e80c96c4ed604e94a6f95d438f5d7c86adaa59f7ee 42c550217f3cd51f79800c283272b51c6451350ef35879fb50025ad772b36ae2
mpw-005_MM1_reticle_overview.png
the same image scaled to fit 4096 px
4.9 MB 1258525b1e099f225e2c6497b23f4cc81714b4310eb4df9a8b9189405ef4d0f097795c5b 5b26b092becfd69568c228e3b08cf5f146c14b517e2bde69f160e6a98a5a4fb5
mpw-005_MM1_tiles.json
where every tile belongs, and its checksum
13.5 kB afd05cb6862d20de4bd26a46b2c023b1a9a2b60675414a24f4ebcd76fc527b67ebd5252d 2d71b8ed548187e1c57332066cbdfdbc8cd5d6af5f52844e594a854849aa6212
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 4.7 MB0272826433bfdb342e0881d0f99a4fdb9b44c132681af513e107e40079e350e1
A2 mpw5_cache 7576×107767576, 0 5.0 MB88a31d688b0350216b57c87a54a7adfcf966c5cf51fc9ce3af2ba6ffd28314cb
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 8.2 MBa07dc82412f187d34d3f396062b71f67a5b91deab03df7b3c00655482e43f022
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 6.8 MBba4677e61245731ff67ddd971557dc85c9d6a08d894bedd9d79e2771d4ad7381
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 6.7 MB6e92dc19d992df24fefc6d2637b570dd6e1bcc1d9a7a38bcefcdec4637dad588
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 7.8 MBc5df8ddac6307364d2ee5c7296a0e07dd2de59b53322b37adb2c661a76639f21
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 5.3 MB0f16dc1856562cca953eddfc20488c09a7c48f9b36f1460ec6ca233d9c16157d
B2 microwatt_mpw5 7576×107767576, 10776 12.3 MBd8815a0acd5117f017ce474441d2afc6ac360f602676cb74db34afce72631382
B3 armleo_gpio_mpw5 7576×1077615152, 10776 5.4 MB6af5daf7b5bdd3c959add79365cb07fd3f6de0d5960a9eccb55bf91ee80f78f4
B4 fabulous_efpga 7576×1077622728, 10776 12.5 MB4b701d610e695034e77e318ab88a130b491beb0ce1209ade4e64b77d4302260a
B5 i2c_controller 7576×1077630304, 10776 5.1 MB1d0e8de83a52218ccb92777a59c4e2796c3f35c2365a6a62b258839c13e29e74
B6 lbist-mbist 7576×1077637880, 10776 7.6 MB42d86610c602eac78b7548340193e006d7f8a3d0a8d8bbe2d3412a2159cd5e43
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 9.3 MBcbff89cbbbca92ba37f52d4ea1004156e8b0ede8c1858b177a6509872b5f10e4
C1 riscduino-qcore__q0_ 7576×107760, 21552 9.3 MB476247767b684942a3fba47a066f576bfd2a817342bf1d136f404e86c2015226
C2 high_speed_vco 7576×107767576, 21552 4.7 MB5dd731d1fdc9de41517f8bd6bdf78a43ced34e76121eb0159517e99e6b5d30ba
C3 openfasoc-cryo-gen 7576×1077615152, 21552 8.1 MB8393edf092adfea5562eb04be65578a50b3d2e8596abcb6e26af3a2c328b42bc
C4 microwave_signal_generator 7576×1077622728, 21552 5.0 MB786c97d490cc65e758a78f590b62c590a4c8ef1189cae0c6ddeb503b8a585512
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 6.9 MBe40974e44296aaf32b1cb37405203bb01513649fb66e1b4850c670d30079b393
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 4.7 MB3fa15858a4eaac63ceb2045546176845c59381ec0233758d97d2e28a3993b2bc
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 4.7 MBc7e8ba9c27e6f9d701bfa7702308770d0d7dcfde9360f9963df8d7d4eca53fa8
D2 azadi_iii 7576×107767576, 32328 8.3 MB63d70f8af8496222f7930cb8a64f4dcec37fe878bec341ee0c38b3903672b489
D3 tia_for_physic_experiments 7576×1077615152, 32328 4.8 MBb598502fb21c08f1843a68911ee97968196e56f06fc6c6d5349c963c59082d34
D4 qf105 7576×1077622728, 32328 7.2 MB55e8588326699ca79aa24ef88961a015acae6b4a0e73b718ea6e58e4ebec4b9d
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 8.6 MB7ed3721da73c63c345147e4b24b90a6347dc9f9d534a4a685ce19cf0cf168a10
D6 junga_soc_mpw5 7576×1077637880, 32328 6.5 MB27cfbaee1a279ad4174435d63915914f7c8bf2b7d5eee68c14e4f0cbcac6d3b6
D7 nand_flash_mpw-5 7976×1077645456, 32328 5.0 MB4a6e660a42bd7923c991d50fc3538d27e17e054b9dc188f5eac748df260163b6
E1 kasirga-c0-mpw-5 7576×107760, 43104 6.3 MB66ec375f4bc84f877ba5ec9605a338cd8edf34577d91f59dc874ef7201fa7e43
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 7.7 MB2085403f4d2eb436191a85812ca0ea7bb66f5f449a136963afc1c0617440dac4
E3 wirelessenergyharvesting 7576×1077615152, 43104 4.7 MB1e560421e9a68fe8ad2cb38a454be7b34f460934d3f82e42539578712bf9d1a5
E4 caravel_raster_engine 7576×1077622728, 43104 8.6 MB8211013f1ae468720701590f4c989db2ed8670fd206b78ce6763f76f2ef271e2
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 4.7 MB52a7b84737638940cdf07b75c49fa12408b47ebda2868b0b34a6833bcb3d9784
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 5.3 MB5defda927f44638288fb4fc5b43a3b240e35879b03b7d8b6ee0d0e1e70b56d28
E7 alu 7976×1077645456, 43104 5.1 MBd5319bdb026bad50a8725ed1be62a7c5e6fc047a0c9255f9ec8ce388a6ca11ea
F1 4ft4 7576×111760, 53880 5.9 MB585589169740e81f8e98d0caf37a27f2abf6d42c91dc927503ef07296609d31e
F2 rng_chaos 7576×111767576, 53880 5.2 MB77437f060ceae8213b3ec59a3203ed671819a030a07acebd7d27b2a626ec0ed7
F3 uetrv-ecore 7576×1117615152, 53880 6.6 MB1e3afd4acbfd6c44f9bfce9b863a74eaeca832851f9ac08b65725c2d423fae30
F4 systolic_array 7576×1117622728, 53880 5.2 MBd785298fffb35c980e1a432fc93b629e00bde54953bc64d9964707ffe12683eb
F5 reram_test 7576×1117630304, 53880 4.7 MB95ecdfe86efa04a7e3cf388eafc1de62b778d15646985ecb05802c852c42e10e
F6 secure_memory 7576×1117637880, 53880 5.4 MBcbff141d1aa3cfb08ea910a7d21c0d9db3cebe14b63da2c2f4a21c1df28a70f5
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 5.2 MB128c9614913d0d076fcb3e132d1582cfdb2115420d915829079ce2fdfe8275fc

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 MM1 on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask MM1

Mask layer
MM1 — Metal 1
Process step
113 — Metal1 mask
Drawn layer
68:20
Layer on the mask-layer GDS
36:0
Fill layer
36:28
Also
Metal 1
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.