mpw-008 / VIM4 Via4

the VIM4 shapes of all of mpw-008

40 of 40 dies rendered.

11,580,519 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 (31.0 MB in total, largest 1.6 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-008_VIM4_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-008_VIM4_reticle.png
the stitched reticle, full resolution
13.7 MB a76a3aa06fa9dd1ddb023723fc9e5ea3e054b0a7ae393e01ffdf73d5152f62cb891383c7 b672cade20be44b91be2982c899d4ff51ae4444f9aa1239630776e788735eb2b
mpw-008_VIM4_reticle_overview.png
the same image scaled to fit 4096 px
694.0 kB 95af0b35d4f3052738e3e9ddc6777660bcbbb1e2326797de277bb3a957759e33c4594d61 99f8f69808f5dd16417a6eb9e3b5e8ad6bbdccf60f9963fc4d229adfc4cbe3a1
mpw-008_VIM4_tiles.json
where every tile belongs, and its checksum
13.5 kB c6809a0ce2c75afdf6e1ecdd931fec96e89ae8ef85bce1644a3f75fde1b93200d16aee38 5f2aaf3d7ffb1e2614d8192b30dbd2075fea3a3d5c4710350bbed015901c690c
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 wishbone_pio 7576×107760, 0 732.2 kBef1815ab6a90690bf0cc6527bd14ab0fcd700ea18655033deeafdcaf535168db
A2 iit_indore_mm 7576×107767576, 0 732.4 kBd4ab9d0113ead2c21a69f5a91b245fe1bb51082b6826e996c78802b6fd6bcdaa
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 733.4 kB51affe9131915b43e63127c6c0ffb28b7962cdb9a2abed802f02c8573b174aca
A4 riscduino-dcore_d4_ 7576×1077622728, 0 768.0 kB9c7b644aa0a5fbd19f5d6bb7777c195a9023f62a42eb305fbc6e3fb8a2bb35a7
A5 riscduino-qcore_q3_ 7576×1077630304, 0 772.3 kB002a00980d15700e1e68f5b9d86c84c1ebbd28ef4104a3b42d3f06af71bfa46b
A6 caravel_mpw8 7576×1077637880, 0 733.2 kB1def7a195f505131dfb8d7ab8f16983b1fd106f0470fcc3dd6724e1abd928737
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 1.2 MBaa220eeb9abcd37f4935cc2cae44d65c6de10c9b7b7b23866a64f29c65436713
B2 sramtestvehicleg4 7576×107767576, 10776 669.5 kB6b8fdf770e36b746418c973a7f884b2a33fdb80d26ac2bd3a1ec153c88b01603
B3 reram_crossbar_rerun 7576×1077615152, 10776 1.6 MB96ee080b2bbf7826b7bf0e64c111fa647d7555e91b93c75051c9663f1aeb6d69
B4 tinytapeout_skullart 7576×1077622728, 10776 708.1 kB3e748a06c11fb3c768bd19d20bc6e7decaefc3b6fb58100164b88c42f7047766
B5 soomrv__mpw-8_ 7576×1077630304, 10776 729.5 kB60957591fe7187490f9863c90db043c6c66a950648dd378172d1a364c5833fba
B6 clock_divide_selector 7576×1077637880, 10776 732.4 kBb06ef7fad0673c1384a8d0faae857f5b3f8dd061e53e8b3a39afaab1ac9340c1
B7 reram_module_mpw8 7976×1077645456, 10776 738.4 kB248f934d4756cd6f8bdf8ebd15c1fa0ca8ec7f8f69619b8c93f9234243b7a370
C1 shapingthewaves_8 7576×107760, 21552 740.2 kB0c03c2a5c5be3d1a562fe13ae85877b3d4240fa57a0cf1e89fed07d15d935bea
C2 leosoc 7576×107767576, 21552 729.2 kB84dcd2f1812b3dedc4c03fa25207527d2ec420fc09ca034f9c092de09ade6cbc
C3 decrypt_aes128 7576×1077615152, 21552 731.1 kBa3221351e22e70ccae8bc08ec0bcc87fa6e0ab5531170627d0cebb88483e5171
C4 approximate_adder_axha 7576×1077622728, 21552 733.4 kBc545886904f54e6f21b297df39692a571cb9ec8a9b81b6bc3eabef5354608f38
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 754.3 kB5aaab92d26a804b40b30a3fd6b05809f119c8398bfb4493a5dd4863738a8c3c9
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 697.7 kB89725adb8997d20ca0c8dd015609179c478e1dd431ea77f72fcf7407ccd9aec5
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 718.0 kBef04b0c3d51a3d7b450b6978ed74e27b6685e239b67514e6fe400ca7215de6fb
D2 hyperram_controller_for_sky130 7576×107767576, 32328 732.9 kBd53ef92cf2ac6c994a6d99abcfc3a7a1e6f003dff1e3f0c61073893feefcd228
D3 hardened_alu 7576×1077615152, 32328 733.9 kB2dd8bfa14fe0fcc8a8d3ce5035d45db5f938008bc341e97323c89f6b77d04cc2
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 711.6 kBf11c9786b8681c13fe75e2ffbee94fd9355da531f8167c27bbb0d2339a1f0d39
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 669.5 kB6b8fdf770e36b746418c973a7f884b2a33fdb80d26ac2bd3a1ec153c88b01603
D6 gf180_vs_sky130 7576×1077637880, 32328 732.6 kBca8900843ca3bc05d9f9a7a2e87cbdbb6d8bfdb7076b0dc5c2c6f190d0b9ffc0
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 747.8 kB01dde5da9ac935e48923f584d66207eea89c5948b1741497b001e2cdf4aab144
E1 iiitb_sd_mpw8 7576×107760, 43104 733.4 kBc0810288f91d0b0a6c1b68d3ec6167795a42a322d0b364facfc1f9e425711a71
E2 iiitb_sfifo 7576×107767576, 43104 733.4 kB7cc7db7243bd61ac033e94110f1c39851a9d668453d98d9d220132c97b7bb646
E3 iiitb_alu 7576×1077615152, 43104 733.4 kB51affe9131915b43e63127c6c0ffb28b7962cdb9a2abed802f02c8573b174aca
E4 ppcpu2 7576×1077622728, 43104 730.4 kB6d216dc64daac3ba46cddcccb67ee1f365629a19515352c2ae8dd66029822789
E5 mega4_reram 7576×1077630304, 43104 908.3 kB9aa7acb5f65dc866f5df09752c857e0dc42259a6f3cfcf4700053282e05a65cc
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 732.4 kB66d4b1e254e24fee3e3aa094bde90b0f876e075ed67241c2f13481c71494bc00
E7 sky130cla 7976×1077645456, 43104 747.1 kB8de93d93eb304078f3f79865a4fd3f5edfeb384e9c62dafde93bec93716890b2
F1 uetrv_esoc_v2 7576×111760, 53880 737.1 kB4e192c528f94685a98c77f52188a31c6657e8a4fbe71c729387c7482d9750430
F2 sequencedetection1010 7576×111767576, 53880 737.8 kBc1a07da28759e1c0a2e7b848a28f7242b7b9b0c3b6b1ada48a26a3238ef3301b
F3 testasynctrimux-mpw8 7576×1117615152, 53880 736.9 kB7e4e8235c8e2fd47977bac058183943476d0b08fe121591e937e1ef7c09beac3
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 737.0 kB52e27f5228841e85527f844cfb39d2610ca7270d54ea67d89febdb0b5366a9b1
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 737.9 kB87969ad0a54268c7a1a0bf2f547beb88be8766afdb2235838333b7ffd1a63a76
F6 arbiter_puf 7576×1117637880, 53880 737.0 kB52e27f5228841e85527f844cfb39d2610ca7270d54ea67d89febdb0b5366a9b1
F7 unigate-sky 7976×1117645456, 53880 749.6 kBc78092c60925ba889e81b7327546fa2f4ec0fde60575abd9366deae72dd4241d

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 VIM4 on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask VIM4

Mask layer
VIM4 — Via4
Process step
159 — Via pad mask
Drawn layer
71:44
Layer on the mask-layer GDS
58:0
Also
Via 4 (M4→M5)
Used in SKY130
yes

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/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.