mpw-008 / VIM2 Via 2-PLM

the VIM2 shapes of all of mpw-008

40 of 40 dies rendered.

51,826,231 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 (32.9 MB in total, largest 1.2 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_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-008_VIM2_reticle.png
the stitched reticle, full resolution
21.6 MB 37b299d8c46102ebba17b21d56ccc82220a83e87f21a6c3521765681b0d1eec7339b4a2a a8365f44bba70010cbb22c511658170b7647ea25a0f3c5ede0d3690b6d2475eb
mpw-008_VIM2_reticle_overview.png
the same image scaled to fit 4096 px
791.8 kB 17c514cdd4bd9eb186d4eb85a18be76275454590f2072be68de1a45f0eb1b80ea9f40ba6 956413127b472f71567bb1f6ba4132905c1cfa12238e12c3c09206f1d7abaa42
mpw-008_VIM2_tiles.json
where every tile belongs, and its checksum
13.5 kB c47767b483bb5364dc81775055513bab6263fcb2433887739bc5c5a9ddc8c066dc8d6f06 481c0c65f6eac429401200a67e461ce3cbe798ba9d9b30fdcd6998c618f7711f
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, 0 783.8 kBc7de4da0c964321312d636e900066916a6c12a9acd6602e83f7a3d8364d9cfa1
A2 iit_indore_mm 7576×107767576, 0 685.2 kB6f1b643a4f06dcda08adb22687f8ee3cdd14131cae4b2ab1606831ddcb6c60a0
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 692.0 kBc4b82d8730e3d66eab28d40dd90ea9bb173f66414901d00ab9299857c9455aa1
A4 riscduino-dcore_d4_ 7576×1077622728, 0 1.2 MBc1599abd819f2542dc20b0ab6ed387b40e778ca70e4a3ee87e4a1cc41f139346
A5 riscduino-qcore_q3_ 7576×1077630304, 0 1.2 MBfebe5ad5b5fa7da9e59d735bccebee4e9447da84d83a7a702e21c062beef80cd
A6 caravel_mpw8 7576×1077637880, 0 695.7 kB09612b16cf1b756df2996011ce49965f8d05aac221579fe0465362f76a39c2c6
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 787.9 kB806884a933ae933f917df70f3a9fd5088ac934cabc62714eeac2340d9f3eea15
B2 sramtestvehicleg4 7576×107767576, 10776 657.3 kBb5810afb40b5e4e65fb64e8ec01b5a5bcbd96d354a206a6d674a4c879433ee2d
B3 reram_crossbar_rerun 7576×1077615152, 10776 868.4 kB2aa292c27ef3b2d18142c9a8fcf61a466d7f06041ba7ed4c39bb6f06b35a2798
B4 tinytapeout_skullart 7576×1077622728, 10776 1.0 MBbf630534289e18aa8388f5de36f7d5ad5e74ac913d4f9df77b459435a9795f84
B5 soomrv__mpw-8_ 7576×1077630304, 10776 1.2 MB3aca54df73e6480888cf38d9a9eef8f1366fac28c6f103f77b158df9e5b062d5
B6 clock_divide_selector 7576×1077637880, 10776 681.1 kBd13a6dfafffe0f030456335d2810a9076b4ab8f6ee90ff4b68bd92f186396bc9
B7 reram_module_mpw8 7976×1077645456, 10776 694.2 kBcbd9ecec85262a672911ea949dda956c7d313d4a0723a99da38d3b47136bc439
C1 shapingthewaves_8 7576×107760, 21552 914.0 kB45742e3b63ba9206c6fdcd2dcec9114879864cf62df7ad71545612cec8dfabf1
C2 leosoc 7576×107767576, 21552 922.5 kB179d157ec4c94a06aa565122ff22f31895fa3b33f3e7932a9bda2f61fce02f4b
C3 decrypt_aes128 7576×1077615152, 21552 904.1 kBbf6f74ab172f0de3337973d3af3d33baa46ba32852544060dd7e5a29f50db746
C4 approximate_adder_axha 7576×1077622728, 21552 691.2 kBe3089f1b21148f9821daeeb0411e6af7ef1c3ad71e2176792daa4cf4d02d4466
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 861.3 kB46e86f5acd5a14a71839fd85aa51f0b8210c51a3300f12c999c0869b25f2fe82
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 680.9 kBfb60ef5d277ed7bef40f1de20d2f5eb707296a318f9461441b1aa0ef527098dc
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 1.1 MB7f0235b62f36e3c606451aa412ebf0de944f9de557af6974559a69d0b35bc0c9
D2 hyperram_controller_for_sky130 7576×107767576, 32328 686.4 kB74c60f834b671d7b6d179b12eae345b928f5b36a16d23b910fd5911e8b149479
D3 hardened_alu 7576×1077615152, 32328 721.5 kBe1687a0df31cc90b28873f4498333eb09c907a07c1d6e6c9f4cbc8435cb41506
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 906.4 kB5d9884b6bd670090d94b72e622bfc3d188fcc2f3a223a54acbe61ab988884f8f
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 654.2 kBb4f0c4ebdc49c24174a6780b82b26e7364117ecf7e8dd2f7c5ac46ccbfd843ce
D6 gf180_vs_sky130 7576×1077637880, 32328 880.0 kBde0182606b59a5d146a44d2b15decad4a4c2c1c1e4abbaf4e6d2523fcf92aab4
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 701.6 kB2eea50aab3f2b62386bfadcaf9b76a2625b37b1301f9f5b58819fef866654f0a
E1 iiitb_sd_mpw8 7576×107760, 43104 691.0 kB960f40bbf0717224e2b7f78522bc57f5d3f2735465914e76c442d03d2810a48f
E2 iiitb_sfifo 7576×107767576, 43104 692.1 kBa3d48bfebcadbc873dc9464ff8e329e57e4ed7d17a1fec4a7f83da9506c2153f
E3 iiitb_alu 7576×1077615152, 43104 691.3 kBa75a3bd9e4230e3836603590756af761348229f472a16b3199c2f5d7b9336909
E4 ppcpu2 7576×1077622728, 43104 1.1 MB274af084b394e565a2e3b80a6a12fa617701bc53173f7e57e82853e259fba067
E5 mega4_reram 7576×1077630304, 43104 1.1 MBf3e1e3b870bcf538be587a6a985121085ce35dc5b746ab6ca695e84fbcecb5f9
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 687.6 kB313642533d3e55a7a608fe36253efe96cc01423f3b39a3554fbdfd6becc43069
E7 sky130cla 7976×1077645456, 43104 695.7 kB29a3e3e8bfa5b9409b5cb3a9de9671d6ea1a65e980ba71e644fdf39ccb060d6b
F1 uetrv_esoc_v2 7576×111760, 53880 863.8 kBd6b8ca2d8d6eee7da005d29265be9dfd7003540643189680510074e67e425895
F2 sequencedetection1010 7576×111767576, 53880 695.2 kBe379c04043c908b224f4e5a8f5acb7ccbd3e2a3f2f426eafc11899364be0965b
F3 testasynctrimux-mpw8 7576×1117615152, 53880 685.6 kB948e757ca2d6e017226fc10f0c906c1b1cafdcae125221acf1224636bdd94b93
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 685.5 kB10d87d7d371c0f226d77f54a8c6d80f9ab255cce06666e99081197ae1c0a4ec1
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 694.5 kB206baa32149a648345ef068d085efd9e7fcbdb055c800f3f406309ac9e6168c8
F6 arbiter_puf 7576×1117637880, 53880 685.3 kB619f06f06ca396c5b973ffe2862ffb74c7b975a58ac9ce18680bb5905bd72723
F7 unigate-sky 7976×1117645456, 53880 875.6 kB970690ab406e0de8c67e255247d07b1fdf9d3ff6d00fdedaed04054907a6bc15

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-008

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