mpw-004 / VIM4 Via4

the VIM4 shapes of all of mpw-004

40 of 40 dies rendered.

9,639,382 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 (28.5 MB in total, largest 752.1 kB), 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-004_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-004_VIM4_reticle.png
the stitched reticle, full resolution
11.3 MB fe1a76e6e3cc82dea68065a017ac47f6b87646a132eaa8aebcc631d81c8115ea0c588207 dbce998dcb3b5d33c22593aab722ef721828c99a6e5d98ff718613b45c351c87
mpw-004_VIM4_reticle_overview.png
the same image scaled to fit 4096 px
576.0 kB 4db70f0b57b46ffaf6895cd3f5d735c0d5fb1e34f840052f019e750543e108b676c43158 aa53d9b89cfa137690f6fc8d16b048cd489a7eb7e0290929791b1db7c771764b
mpw-004_VIM4_tiles.json
where every tile belongs, and its checksum
13.4 kB 0da7b37c0bde0444b9a904f886868fbb4870c6bfc42c27a24e2037689366f02af48b76e1 c2fbb27695eb66e27286aac68e71a2221ee71a7f23f487934525472319ea176f
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 zero_to_asic_course_group_submission_mpw4 7576×107760, 0 721.8 kB1589c316e25845733b26cbc6af60f518d8c97099206ad11d4fcff11bd699d337
A2 zero_to_asic_course_mpw2_re-run 7576×107767576, 0 721.8 kB0d08d750834f40f2cdfbcadec85b6e83007cbe7c395898d6924bd6396559d1c2
A3 cmos_only_self-biased_current_source 7576×1077615152, 0 661.3 kB58739af58c19dfb6a76581f752a199fc0dcd6ac6541f83bc5242ef80c916158b
A4 pifive-soc 7576×1077622728, 0 721.4 kB44732487967a830e2915cb76c94a29e9b84d59056c2e3e9cf50631dd49f29677
A5 ram_generator 7576×1077630304, 0 720.8 kB6ced83340bd791e49c46f4d673109488d392b3fbda4567051fb2700363978c86
A6 space_controller 7576×1077637880, 0 721.7 kB39792fadd01e239fe14a43101c812d78cfdf00543f86762855a61aec27485f5a
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 randsack_b1 7576×107760, 10776 722.0 kBf47b8bf1dd11e9e4452f750cd7c6fcb8a136c8c3482f88a61f6b24b6cd988b1a
B2 4t1r_testchip 7576×107767576, 10776 692.6 kB184259cb5ef838de007c5483709e3bb956077c10ceda95f8f054335f4005659e
B3 yonga-turbo_encoder 7576×1077615152, 10776 721.6 kBf0c15e4a48e258db7b8cb55044310302cfb1c7344692830db2d2d0cbfb92462d
B4 icesoc 7576×1077622728, 10776 722.7 kB414656a12ff336d40ca3f60a204543b31416bb4682d82714e01f8fe8b1a1a631
B5 key_value_store 7576×1077630304, 10776 721.7 kB5353f7eb2f23c154894624c430d632de72219587e1b5439575f04701d120bc71
B6 subservient-mpw4 7576×1077637880, 10776 721.7 kB6f46c3b7f701bdd7b4cf93ac3dc8336043171d8b6fb365b27f97640002c3e2d2
B7 logic_bist 7976×1077645456, 10776 735.0 kBec5571b0de38097e53f2cdf9cf367e95c36999cc9fb991b1a36edd9cd4dd1927
C1 riscduino-r1 7576×107760, 21552 751.2 kB7dadac9213d9385630b7b530749fb580116d4fbbbc3d4806c7acc932271a2a9f
C2 template 7576×107767576, 21552 721.8 kB9350e4c2623ea73c7f8587a9220e334814c6fda58cde33e8860c6e2a8df9b9f3
C3 vexriscv_soc 7576×1077615152, 21552 721.2 kB7a64f34e3156017ecd61484ebce631c73b2289ab5fc7d1869182821665fc7aa9
C4 ring_oscillator 7576×1077622728, 21552 661.3 kB58739af58c19dfb6a76581f752a199fc0dcd6ac6541f83bc5242ef80c916158b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 an_mssro-based_vcro 7576×1077637880, 21552 666.3 kB7d1d1ffe3f992f416230fa1397c3d56b89663150298b8d435a28ec188f67e923
C7 approximate_multiplier 7976×1077645456, 21552 731.1 kB39bffdaee23b8d9cc6fa2eeef0b1103f8872b65a429ba6d1a65aa5cca7eeb69e
D1 icd_fast_nu_mpw4 7576×107760, 32328 672.0 kBf5711c53fa251d8829f7cf71f99eb822dca14c73980e4a633b5f6d72c93f5de0
D2 rram_testchip 7576×107767576, 32328 661.3 kB58739af58c19dfb6a76581f752a199fc0dcd6ac6541f83bc5242ef80c916158b
D3 rest 7576×1077615152, 32328 715.5 kBd1efee40ee5ff4e799ac5720203a2d1ab522652c2c386e5811e1b8fb140e7c93
D4 fault_tolerant_16-bit_alu_design 7576×1077622728, 32328 701.8 kBe58217c02d84ae6691e4be6a7b2f07b65b8fba07ace10859d5bc82d240a6fe46
D5 sky130_radtol_test_chip 7576×1077630304, 32328 670.5 kBb0f920cfbcc1235bc5d46464e68c7e14d849e955e54b8c64542a2d36f016b630
D6 two_stage_cmos_opamp 7576×1077637880, 32328 661.5 kB7577b5df932f7753cb73512dde4c004436fb5b47e343b13b46ad8ebd7c980624
D7 ks-guitar 7976×1077645456, 32328 735.0 kB074a0b55a6ecef44d1eee2b7469aa915cf2d39b9a251da4ad9bdc02e4e22e0ca
E1 ks-guitar-2s 7576×107760, 43104 716.2 kB310b950ad85293a9a5c6b675989cde030500ad7341f00ebe3232c0fc37aa76c5
E2 yifive_a2 7576×107767576, 43104 752.1 kB72f19eaa3a8befe9afb492cd273ea73aef65ab29f9905a97110d8748b1b8d53b
E3 coriolis_test_soc_-_mpw4 7576×1077615152, 43104 702.8 kB1553cbc9a28531359a812bc88751f0db4b24db4a4c4c48caf9945614f5f4a402
E4 sar-adc_and_analog_circuits 7576×1077622728, 43104 666.8 kB240650e0496079aa2a419caa28d8f30b60b95c37561d624a6f174be47566df79
E5 junga_soc_mpw4 7576×1077630304, 43104 721.2 kB7a64f34e3156017ecd61484ebce631c73b2289ab5fc7d1869182821665fc7aa9
E6 ibtida-ii 7576×1077637880, 43104 717.0 kB50c2a00d3e35f224800433f5c5c457229025c31415c354848eb9e280458da799
E7 azadi_ii 7976×1077645456, 43104 730.0 kB017239a2b7ff9195e1751aaa0b2a72b5a6ba3e54c4a73a37c4f5a8e16e156e24
F1 open_source_configurable_fpga_fabric 7576×111760, 53880 746.7 kB0ce86aadf8e5705d8f0c2ba980e3a8434e6fc799ae603421268b7f4733f8c219
F2 current_starved_vco 7576×111767576, 53880 666.2 kBaf239fb4f0978e900ee3eb56f3708592234fef7cc0899b8320300f41bb2d75b0
F3 updown_counter__test_ 7576×1117615152, 53880 725.2 kB53c5e432b4cff3fd69f9bcfd37c00826949994b4f335bfa9ea9b6d328f304ac3
F4 skullfet 7576×1117622728, 53880 666.2 kBaf239fb4f0978e900ee3eb56f3708592234fef7cc0899b8320300f41bb2d75b0
F5 cmos_rail-to-rail_comparator 7576×1117630304, 53880 666.2 kB57472eca406c424c2b8298bea11c972924fd1e0ddfd1b278092318543107bda2
F6 kasirga_k0 7576×1117637880, 53880 725.4 kB8c8e6d7f53b0a32aef59958bf2d92e60f30e2e5c3c2b874f987537f8fca4e023
F7 iotestvehicle 7976×1117645456, 53880 697.0 kB1988d2441740295d548c9d94a1172af22e9d59b1b67bfb2c7509a3cbc6f01a5a

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-004

Resolved: reticle 5CS8010AC, run mpw-004, 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-004 — SKY130 MPW-4 (original)

Foundry product
EF MPW#4 (+ 'With ReRAM' split lot)
Reticle set
5CS8010AC
Run date
2021-12-31
Submissions closed
2021-12-31
Projects
52
Fab lots
4216266
4216266
4216266
Identification
confirmed — Lot records: EF MPW#4, lot 4216266 = 'SKY130 MPW 4'; the set is absent from the 2026-08-25 return shipment (destroyed 2025-06) — see 5CS8018AC
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-4 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-004/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.