mpw-008 / VIM Via

the VIM shapes of all of mpw-008

40 of 40 dies rendered.

96,616,220 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 (85.8 MB in total, largest 4.9 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_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-008_VIM_reticle.png
the stitched reticle, full resolution
47.0 MB c51456edee807cf28b2461047ca11d5a1ca3a62190b2e97886111e00199cb72e3c8de93b 4f2f8a15b4872798d433fa9a162c7ca18a49d72ac4571e74e4a21c923b6e03bc
mpw-008_VIM_reticle_overview.png
the same image scaled to fit 4096 px
946.8 kB dbbcd4064e20183cfa1d69c746bf5a5bf97cd8fd1e98f26112909d3b368512ee632d3a6f 902984606d4175509e3d6aafc021be7a3eea7fb50ddc5a3e0f5ba083e0d3fdc1
mpw-008_VIM_tiles.json
where every tile belongs, and its checksum
13.5 kB b6eedbeb80731b1f97e4259361a30c879ff1dee44d4e567f21310f863521ac61d0de91cf b05467421bd5f2bf10f97c7f0aab3677655ae112a216cc724e6df5ab290f80d9
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 2.2 MB89d7cba9cdc033612d5b4f70e0f579a734bc389186555b7091f98359c7c05339
A2 iit_indore_mm 7576×107767576, 0 1.7 MB0b068ccbd403441acb69bfbf71c078dc185d45207749b2847748e338e35ccafd
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 1.7 MB16cecf4b1bfc6b62d2809ae5d564fbceb85a903539fa00d677ad88494304bfab
A4 riscduino-dcore_d4_ 7576×1077622728, 0 3.6 MB63ef698b79f8ade3983de387296a1c5de329d8394ceabf624518076bd32eddf0
A5 riscduino-qcore_q3_ 7576×1077630304, 0 3.7 MBdeb023fb098ecd27d2c3c29c95d798da29aade63dee62c7d25a0936e5e1d3c7c
A6 caravel_mpw8 7576×1077637880, 0 1.7 MB3a9554d2c7eb50356f04534f312483e4b08e00f2bb253006db2ae653794ef928
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 1.8 MB8642691cb74cb8b0a11b4cad699b942c58769514346b045bb6e632f6197881d8
B2 sramtestvehicleg4 7576×107767576, 10776 1.6 MB438d7501d02a3e3a17f3c7c56fde308de3ac8a20d30348e97957906020ac94fa
B3 reram_crossbar_rerun 7576×1077615152, 10776 1.9 MB7281f082bc10f607ea0505b6a4e31b0d42b9589077ad20cfcfff5b3f476fb96f
B4 tinytapeout_skullart 7576×1077622728, 10776 2.4 MB8acd2cd6560c32767a6f32d574369c0cd825247017a1d8795e5680ade16edd75
B5 soomrv__mpw-8_ 7576×1077630304, 10776 4.9 MBadf5ad442963eb52292254e1ed0b776e04b8e9e653fe1a8727906d69471a1411
B6 clock_divide_selector 7576×1077637880, 10776 1.6 MB4de269639870048e8ceeae4d71a279f299b9018cbabc9ee4092d55d557223b17
B7 reram_module_mpw8 7976×1077645456, 10776 1.7 MB707f4fc8c220771bd69ec64947ead7e1bd9a342530bfa28327acc40265126332
C1 shapingthewaves_8 7576×107760, 21552 2.8 MBffb82dbeccb688c5b9e8be8f3554ffbf3d5c09582b012fc453dcf28e8016bffa
C2 leosoc 7576×107767576, 21552 2.1 MBc86bfd31f4d283077513d5a39dfcb308b6f364fe370317166fbbf9f18c69b177
C3 decrypt_aes128 7576×1077615152, 21552 2.4 MBd0e35cd903701cc3853049cbfbd30fa42183a53b6f370f335d0e018a5dcfbffa
C4 approximate_adder_axha 7576×1077622728, 21552 1.7 MB7ee1400d251037def3f97de1593615d4a6ebe0f81de60fa997d9a1554b2cfef8
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 2.3 MBadaf9f097d7bc8bfbb77b02b7531c36b64611cefe38589b1b172562d84c18bce
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 1.6 MBeced11a6908a1876dafd4411683b6b7a7d25c39fc4fc92dfbf046a86158c593b
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 4.6 MB34b48ea424f0901f6dab05f59c21cb66555e4b81c6a50d9d5b9d491c1d285146
D2 hyperram_controller_for_sky130 7576×107767576, 32328 1.7 MBd0905774a95736b4c406353ae4a8feb6ff9bec4c8703693b3733e9743d706496
D3 hardened_alu 7576×1077615152, 32328 1.7 MB51a33c1b88ec40c8611b4a46179706c16d3ae1de7211d19b3b2e8c7347e9e83e
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 2.3 MB24ed3900835b78373033d88bd633814898828a16a93b5bab645545ce9a4c6082
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 1.6 MB70049782647ca53ca5d439f6ccf5d1787a3940b624f953a82fe50c60445b05b1
D6 gf180_vs_sky130 7576×1077637880, 32328 2.3 MB48bfcb9130752f478fbd6558043b9dc82ffcc0bdbef9bf19e7b9939bf0fe4b92
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 1.7 MBd84cf0288c2dd247f58c1296d6aabfaf63e7654dbe79d892602aa56bb13791b7
E1 iiitb_sd_mpw8 7576×107760, 43104 1.7 MB9b60eab333705e47ad4cfe88698e6fff9b01b6d14a22a2991b8fa25c7ca77de1
E2 iiitb_sfifo 7576×107767576, 43104 1.7 MB9c417666730a356232d77ffcc71833951b080c2a61928823ed68fd68c6f20c70
E3 iiitb_alu 7576×1077615152, 43104 1.7 MBeb60c48ba349407cd70e17667562929f95fc6c7e65ebddb2cb6b5e0c895eb7d6
E4 ppcpu2 7576×1077622728, 43104 3.6 MB4ad785b2a8c2090d3467062cffc25711c2d10e2c12489c4a94d5376451c9cada
E5 mega4_reram 7576×1077630304, 43104 2.2 MBc77e38ff7cb61bede1f3da30985862a64ddb511220d8ba7f01cfa2917f5146ea
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 1.6 MBc695dcccf8c9628d7a96d77471b22bbc068b00cacc96b6a04dc3c2c2e42b5cbe
E7 sky130cla 7976×1077645456, 43104 1.7 MB0f9d7ac09a62720d74fce49be4712a0c59d44892fa01aa10430b2b07cd43a436
F1 uetrv_esoc_v2 7576×111760, 53880 1.8 MB01012e8b48241994332af17f87269790f37459e7b80a0747949fd3ee1a023443
F2 sequencedetection1010 7576×111767576, 53880 1.7 MBf4bffecb13bb01e7436c5109811b79e132656efbd13b8759b5c56776704c2ae6
F3 testasynctrimux-mpw8 7576×1117615152, 53880 1.6 MB7515fddac6ca2772ac6ddd5e862c62903c0be96c3675d72ded4e13b7e8b27c80
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 1.6 MB68cce0309d382db45380c5d58be9ceee7487c33680d8ba4f536d1dd78802605e
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 1.7 MBa8252bcc0c71331a4370d27e079c6b9f09704963823aaf75951e6c19bfe7bf6f
F6 arbiter_puf 7576×1117637880, 53880 1.6 MB511d4a31f341616fa661c239870d0380af50aab6503e11c78fc5cbd212dc0dab
F7 unigate-sky 7976×1117645456, 53880 2.3 MB87623fa16a27c3bd00eac5e37ae532806129e28380d485ee12cbede18512ca02

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-008

Resolved: reticle 5CS8017AC, run mpw-008, 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-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.