mpw-005 / VIM Via

the VIM shapes of all of mpw-005

40 of 40 dies rendered.

67,190,811 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 (67.7 MB in total, largest 3.7 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_VIM_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_VIM_reticle.png
the stitched reticle, full resolution
36.9 MB 98531286fe7b8797151590605b8a620ed05f4d202a4d4634a8a04f193c701990bbd4e252 81af9485ddd0d39c97b98b3b5ef6821a83f7b2f046c9f0ed5923d0d254074592
mpw-005_VIM_reticle_overview.png
the same image scaled to fit 4096 px
788.7 kB 8ce54eb101e5fbf8c8e422bec1fbf2a1aeea8c1a377b3b5ea7665585b384cfddfa81829b 825084da9c4d3118460a34500345ed2ce937ed343bc318e9ae315aa9703aa0de
mpw-005_VIM_tiles.json
where every tile belongs, and its checksum
13.5 kB 693df8fd040eaefbbb6343e3e542fed9511ddc515ae932b34892b3d00a246b4f24de8c5c 9709bafba4f27e530ea9d55918ce932474a37aa5cb463554c35bff099f5f5439
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 1.3 MBe9d4c8e0ce12c2c069017f5a4b407b2d6ad4f071f278c946ac6675475f4a8399
A2 mpw5_cache 7576×107767576, 0 1.4 MB90f027fbff56636bba3ad1224ce016bb8a2b18e62ffab47b20e41e18d3348269
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 2.2 MB9c22f7f5d8effe20913c403d7b5c13634a7d6cba463c95f23841bab806471dbe
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 1.8 MB1ba6f2edcf1f4bb799d5a52d91ae88458886cf2d7ba3c7bfa20b15a2b550353a
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 1.7 MBf07d0a0e81f0989f4a2cdac773e988f2cf3bcb4fd401e5f7a1b4d8c386986830
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 2.1 MB5ceb2abe7fe1ad916fc21e1daa4076f5613e76aa52c6cdfd03d2ab475dca652c
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 1.4 MBbf61868310d5936e87f78e079f9956053c2649fbc374ef42004b03e554afe8cc
B2 microwatt_mpw5 7576×107767576, 10776 3.7 MBaa8970adaed2409fdc8be1b73008de4be12b16ac550245e0d93907e3089a1717
B3 armleo_gpio_mpw5 7576×1077615152, 10776 1.5 MB44dae608a93092ead141422f18e4fed198c93211c54d8a806b071bf6fb9fb696
B4 fabulous_efpga 7576×1077622728, 10776 1.7 MB9f4c1aa2ac5ce9d96e3b39ed50c7b6e666d17caf3115856f2fcfa80359102c9f
B5 i2c_controller 7576×1077630304, 10776 1.4 MB2aa7b498d4282969c266f1940c3dcb7a81d6d96a0ea25afc87411a7f16765655
B6 lbist-mbist 7576×1077637880, 10776 1.9 MB8a84d6374767b22eb8b405740710d456e67bbcd44563a2ff8d3d537ae749c92c
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 2.6 MB1d09ab31e24f9b843b0a9e9bc0b64addbb458f665da79ffc842767d2b605dec2
C1 riscduino-qcore__q0_ 7576×107760, 21552 2.5 MB4bd8e6f7c6bf06cf77cb8790ac3bc158696eb02deb9ce10bd1fc3ab29f70b3dc
C2 high_speed_vco 7576×107767576, 21552 1.3 MBf5f7cb8a04a1c0c0dee3800c51f28d5f82629533c6bcd7fc337b4ba66c81bfa9
C3 openfasoc-cryo-gen 7576×1077615152, 21552 1.4 MB1265253a61647f1cc854f7ec1d1b4c7a60c34ffba0b85329da6de38df57859bd
C4 microwave_signal_generator 7576×1077622728, 21552 1.3 MBd27178b5242565709f062a8942c83d937a9e48e78b7d8508d423cf8965664ae7
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 1.8 MBb7d61efcb1ddacd49912328c186d16976ed68790e08fcfe7ebd485621ceb13ee
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 1.3 MB143c4ac0017aa7cc96de58e00f1c8ad9ddfc133d649ac2b692e827bffc77fd03
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 1.3 MBc74485acc7af56c2febab0da41b91d4995be21808fb2966f38a5404652be22dc
D2 azadi_iii 7576×107767576, 32328 1.5 MB5755427ef32c78b72f75c2507b660022dcfd37e91d8a1d41f99c9f02d5d6ff18
D3 tia_for_physic_experiments 7576×1077615152, 32328 1.3 MB32047e440df10eb4d10680a115535e5fd863a75dc46c9d54e2f077309754ced9
D4 qf105 7576×1077622728, 32328 2.0 MBdd2fe5bf65f305a07f4186d62e1c4fe9326dec1ef56f96c6ffc7cf2d05451e75
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 2.0 MBbdc4ee960fd12d6f1edc6b2bc5d481e5aee40dc69554508e954f28bb85647d71
D6 junga_soc_mpw5 7576×1077637880, 32328 1.8 MBe064a4ef8bd6a3592fcb1168ebd4b0dd683e60feca133156e33493c9ed4bb374
D7 nand_flash_mpw-5 7976×1077645456, 32328 1.4 MBe58f8bb51f3f9726569cf24efb77b97c7ee6e6b1eeedd13910d1ddca24df1dd6
E1 kasirga-c0-mpw-5 7576×107760, 43104 1.7 MB9853ff5a13f4cd64004782b71f38e5d08d3816b606ab6589d0605bb8fe79777d
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 2.2 MB087c26d646320789071e1a1658c4372b2a2d37ae6e9001e6ccca6413906be168
E3 wirelessenergyharvesting 7576×1077615152, 43104 1.3 MB2220fe5313030ae181ab414595f64bae5da6c82b151d7bf86455390a5339d52e
E4 caravel_raster_engine 7576×1077622728, 43104 2.4 MB1f2a77cc6b93b7368af1cbea0741ba3abc306f118944ff61478e0607920a0eea
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 1.3 MB848fdfc9f8ff8c426af8656315d37b2430af111948b669e37a9c698f186380b2
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 1.4 MBe75501323b8e3b3f52b98d12ea987b308dc70b964a20a6c437ed1af3275380a6
E7 alu 7976×1077645456, 43104 1.4 MB2d12ff724c3515c7672a4a673c568cb4073e569e0e47d8229c71c17a3f270d79
F1 4ft4 7576×111760, 53880 1.6 MB02a7d127e7e6cc62300e656ebeb35d98ba6e63d10e1932be5ec707f72003befc
F2 rng_chaos 7576×111767576, 53880 1.4 MBa175cfdf00047da7273f36495e371ee387c318c27fb5ea3ca7dea3ae553e6716
F3 uetrv-ecore 7576×1117615152, 53880 1.7 MBfca47ae789c8e849e11f77ce27e511bea1fc1a4e856d6dc539de65b7b404d10c
F4 systolic_array 7576×1117622728, 53880 1.4 MBec13caf52606790613438f2917442c0ec73d52e7dcb09a59e8e9b2e239859f19
F5 reram_test 7576×1117630304, 53880 1.3 MB34ed0e74d1ee905148271de41147f3a1296690f153da9887b67f83c123c968d8
F6 secure_memory 7576×1117637880, 53880 1.4 MBfe754bd8990fbe18ca36942b5d50ac9579497cbbf71ca0362ff275bc6780a70d
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 1.4 MB4ebf137eb444cbfabdcc99366ecc74626ab54ffc167a7778bf253d3207d200d3

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 VIM on mpw-005

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

Mask layer
VIM — Via
Process step
118 — Via1 mask
Drawn layer
68:44
Layer on the mask-layer GDS
40:0
Detail
in the sky130B RRAM tier this is cviam, the lower half of the split via1; the upper half is VIMC (r1v)
Also
Via 1 (M1→M2)
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.