mpw-005 / VIM2 Via 2-PLM

the VIM2 shapes of all of mpw-005

40 of 40 dies rendered.

33,560,413 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 (23.8 MB in total, largest 1.3 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_VIM2_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_VIM2_reticle.png
the stitched reticle, full resolution
16.5 MB 8acbd664e4edbf0ae4f43a591fdddc19fb1af56a5b26a73e5fe0404a555c1900d0ac09fd 0ba7b7a4a84c8a4eb9ba3e76d9f1d02adcf4f26f7122a11730b6ec66bb845ad9
mpw-005_VIM2_reticle_overview.png
the same image scaled to fit 4096 px
623.6 kB 1a729bf76fc2a7a9a157ca467ddae91e02aa0a5515a530e97fa2837f2d13677462e8acb3 0e00ef8b874f116b91aa34c6bae803868f5eaa7809d962699bd37907819ae1c2
mpw-005_VIM2_tiles.json
where every tile belongs, and its checksum
13.5 kB 783fa339753686fca1b7c070cd3635c1af51b1707a58d0f0a6f23afae76ae168e9d43742 609b168e6338bef60be994be03d2c2cdfc85e0b1aa53e85a8d0d3914ab442701
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, 0 458.0 kB7cc2439e6911311635be313e38f7ccf34bcff1d642f654d7ed60a6acfd60c551
A2 mpw5_cache 7576×107767576, 0 501.7 kBc468ee8ef3d108db2e31784ea76a3b9aea8c2883edea6fe38b9f0b2dc568470a
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 710.6 kBd1cdf1a81d98481ae93ee8eee1082867470849fdc25ba53e341ace6f9d7004fd
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 614.1 kB62a5cc3ad7ec7f1abbc601624fa0e2d12307e5f63c326727a5d4e933bf8c1305
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 573.3 kB2e6acf2812a3da18419c651c67c47ad7134ae18468287067484b3af565293eca
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 737.3 kBb28b0c203b316c275e2aba0455575d1b0d0606e9b5e0b6e82e45b9eadd3246eb
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 498.2 kBf69f6fd3e1412c151ed0be477a6e6f3cea867acda7cd29748a4514368099b324
B2 microwatt_mpw5 7576×107767576, 10776 1.2 MB4588f872f88e16a0d6a0bb31172e8a490d10f0adc1962cee372ed7c6ffa90f3e
B3 armleo_gpio_mpw5 7576×1077615152, 10776 605.6 kBe2f775f48f466a353ac4c1d77d5ca7faba7d783d605ecbbee637a749b1818001
B4 fabulous_efpga 7576×1077622728, 10776 1.3 MB0f5633a69bb321409691515ae8d56e281279b9ca4b57cb3411e70cf461f549af
B5 i2c_controller 7576×1077630304, 10776 491.2 kB302b178136cf9199d6bb1e0bc9f3c7e22a06fb0cf63a87b3bbc2a907e58db5fb
B6 lbist-mbist 7576×1077637880, 10776 654.4 kB956a156ea7830f93b2ca0fca907d31deb1837996480e475c953968639732f851
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 758.5 kB5c9e178bd338d77a1bddb366a86516403e83e703cb741e0d7bfd0763e38a3f24
C1 riscduino-qcore__q0_ 7576×107760, 21552 747.8 kBad39ca166737913a2d09ab937cdf0dbbff27aa5032de3c48e8c809d2d17e995d
C2 high_speed_vco 7576×107767576, 21552 458.1 kB150b887aad977e4933f2db08f4ac7a8042015f0fde064bead5895a9f6abb9c37
C3 openfasoc-cryo-gen 7576×1077615152, 21552 536.9 kBcc880de997738967b04a626f3c880d940eaf6d29b882ea20649a9ba998282341
C4 microwave_signal_generator 7576×1077622728, 21552 496.0 kBbe1097f33178060ef9c752c26dc4b05cfe25d67e82f7fea0417e1d4120c412fd
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 517.5 kBfc72c793a5164c1e92e1aa6446ea857f9a5b0b2a551c7075606be83b3249a91f
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 467.8 kB782c8d7732a00eb9eeb968b8aa71ee4abf32aaa1c6dd8e4fcc7fbb578953e701
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 458.2 kBa700586f4dd5b0a4e704cd334d3f8b3479f17fe87987176029030641134a4f1a
D2 azadi_iii 7576×107767576, 32328 785.5 kB284147cf2aec030529aa74495f44413ded53c6fa22dc0d9c216477f70a944c01
D3 tia_for_physic_experiments 7576×1077615152, 32328 470.1 kB635c841e5b843de489e313fcc76b048b9e0c8ac48b8c7d077db09709c6824605
D4 qf105 7576×1077622728, 32328 671.7 kB5ee62d1ccb73291b69f3d9197861381f6023de8ea5ea9e67aeda54b41c33df0a
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 560.1 kBbce82508d27e674a3aed518dc0e1ce2dd32ae593200df88129eeeffeb966f45b
D6 junga_soc_mpw5 7576×1077637880, 32328 560.5 kBb82fa15dd0763f792e28ebfdda78a943971cd6d3e5f21bb51b1de2df7a4b5ecc
D7 nand_flash_mpw-5 7976×1077645456, 32328 487.5 kBae89da0f97dc10e5d15a384fd856290699a969c71ea8cc35677fb6a1040b5177
E1 kasirga-c0-mpw-5 7576×107760, 43104 564.8 kBce4898d54564cbe5b722ff329ee2577e667069a6ccb1b5154f230cc81f380f23
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 614.4 kB63a23bfa5784f60e44453c4e31fd503ee0db3432ddf7c2071b8b0d391d2b3e3f
E3 wirelessenergyharvesting 7576×1077615152, 43104 457.8 kBb2304afc7248c9d9e079d2f5ae40d25104f654b7728d51e998fd98d26b8fd070
E4 caravel_raster_engine 7576×1077622728, 43104 585.4 kB972fcba781fc4ee79cd16b6aadb6e3212c0ef2972a37bb15ec827320c723a872
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 460.7 kBbd7723605ffa007964ef8285707234c76f71af8ceb4576a721fe63d46997a1b7
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 504.7 kBb8f3c2fbd0c5f3f1c06a32f1d53c78cece61fd76eb913c202df94c9e5c063b8d
E7 alu 7976×1077645456, 43104 501.6 kB677e20ddc04faf766bcfcff7d704a608e4f7c6adf98a29f49771f4a21de135bb
F1 4ft4 7576×111760, 53880 528.8 kB8197484940b609f24e400cd3b19bdff0c6a3b7c1e23fd5ba27f0f19249db83e3
F2 rng_chaos 7576×111767576, 53880 489.3 kB025aeec81d8ee519ea02874c8ae1554f64e723b7bb89685f7b4046b7172e2f6a
F3 uetrv-ecore 7576×1117615152, 53880 590.3 kB7162230240400a5d59987ba27106862636ea9f3460234bb23cbd0bc339033722
F4 systolic_array 7576×1117622728, 53880 500.1 kB9e57127ffa0cd30437a82dedde9e91baf90474461cc08388253b60ad69adf421
F5 reram_test 7576×1117630304, 53880 462.1 kBc30cd71dd51d088f5a04f2b6b142a13442945909e47bfa854dedb7140e9f5c03
F6 secure_memory 7576×1117637880, 53880 517.1 kBc1df557dc776bb92f64ea23cfa4b74ccb45e63fd1218e812a51f534b568b3af3
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 506.6 kBac0f37e897e46867d8ae16b97dd5fb6bee1fece594a3c72595cc4d924cb647de

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 VIM2 on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask VIM2

Mask layer
VIM2 — Via 2-PLM
Process step
129 — Via2 mask
Drawn layer
69:44
Layer on the mask-layer GDS
44:0
Variants
Via 2-TNV, Via 2-S8TM, Via 2-PLM
Also
Via 2 (M2→M3)
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.