mpw-004 / MM5 Metal 5

the MM5 shapes of all of mpw-004

40 of 40 dies rendered.

9,884,815 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 (14.0 MB in total, largest 534.3 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_MM5_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_MM5_reticle.png
the stitched reticle, full resolution
8.6 MB 5e7a0149c8b77a4c1d158e98ec5b85edacf0f1190cbfec36495b08c6ac18caa88e72692d cfb3e53bea69a17dc939c0a9775c730933f8efe3419f6b09079e0c4bb1536044
mpw-004_MM5_reticle_overview.png
the same image scaled to fit 4096 px
4.2 MB 26cdb8518d90725d51b04026e9412b8a0f5cfdd4489270cf983558e358733d035acd98ce 1b978f5ab0d14c0ed0046ad3dcf04fe023b35c4db7ef22957d04f1629b6c9f9b
mpw-004_MM5_tiles.json
where every tile belongs, and its checksum
13.3 kB e482e12285de77a20a7010f424d3fc1e4f7ff72f74d987f2035b8a6cf1c0d12ebd365a0c 240ae2b54eafc50333eba7bc9a535295e6186186f10c84b6f2f55ddc6e3c507b
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, 0 328.0 kBb667be92a508a1ac860c177e4eb012c35131f3a1279afcee20592926064a23d6
A2 zero_to_asic_course_mpw2_re-run 7576×107767576, 0 327.8 kB1d45a07813f81c10886e07d8eb9e996a7d19adcb02c517336ed0bc3adb18e977
A3 cmos_only_self-biased_current_source 7576×1077615152, 0 352.1 kBa7e010cd6a0dd32c920bc842b3c9e772ff89643dcd7879dcc25eb2dcdaa23000
A4 pifive-soc 7576×1077622728, 0 327.9 kBe6bfd2ec7cc7138eaaeb0f39bf870602c61d8aff9af433250d1f1f2a475f1708
A5 ram_generator 7576×1077630304, 0 328.1 kB14b243b619a38b1d220c015981bf6271eaa07420d46fa55626e0f38590c886f5
A6 space_controller 7576×1077637880, 0 327.8 kB7c0d493ca571f37ba0fe9e5c14d68a10148d6ac1968c66f2cdf0faed9d21f223
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 randsack_b1 7576×107760, 10776 327.9 kBe27b8caa2686fba940257b63b7e4f3d80089a956ddda5ee0ccc4ff7e98b0739b
B2 4t1r_testchip 7576×107767576, 10776 368.3 kB3f74dd9bdaa2676612bb1e7148005bd7699f3ff999de611afe2641fc8e68a040
B3 yonga-turbo_encoder 7576×1077615152, 10776 327.8 kB5eac3cc51352889beb7afff4439c07ff3c5d43b1b922c316abf54373a1bbd2e3
B4 icesoc 7576×1077622728, 10776 327.8 kB9b28c056d448626aeef6762ad78b24c06545af73291cd880258694af4517f7bd
B5 key_value_store 7576×1077630304, 10776 327.9 kB14dd1c43a22d35a9a45d7060b5ff086c3234d8296bc83f618d50cebe8fef5512
B6 subservient-mpw4 7576×1077637880, 10776 327.9 kB8fab3128f9c42d8361f048c695e83179aff554b33798c1ad4634b5e8672f39ef
B7 logic_bist 7976×1077645456, 10776 332.1 kBeb6537be32e56ea8941b50677c5191c4a34ddc2c480d92702334b8ce19ff7827
C1 riscduino-r1 7576×107760, 21552 308.7 kB8818225821469a3b9b87ae9b3eeec1f051bd971da0c420a1adab2fd5624d5d15
C2 template 7576×107767576, 21552 327.9 kBbfd7809297a37572ea822cf498daf095d26ecd9a96ca871578ac89f53afc1148
C3 vexriscv_soc 7576×1077615152, 21552 327.7 kB590f14324ff49ac6f15463cd3e1ac69ad187a995cf7eced4b532c9eca2ca4c37
C4 ring_oscillator 7576×1077622728, 21552 351.9 kB156b8681e7e00d45a0c631388fabebd150c70051a54c78a23ea51cd2080ab38d
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 an_mssro-based_vcro 7576×1077637880, 21552 352.6 kB355735a49b3da8c2e14bfb8505fb2a3d46c59b1d9155ee0130b2899d680dbaf9
C7 approximate_multiplier 7976×1077645456, 21552 336.8 kBe5ab12ec0e7b3bd58d070a94233b88f4ac3f90eeb6095589815ecbee4f4419ac
D1 icd_fast_nu_mpw4 7576×107760, 32328 375.9 kB26bcf7add6144a19169171b09f33d180bc00a5a28182d1f58b210189f75c4c8a
D2 rram_testchip 7576×107767576, 32328 352.1 kB679d7a44fd57523f0f2e7aad565d8e0572f85fa2d6a428647ccbd06c3fc9d8ae
D3 rest 7576×1077615152, 32328 327.8 kB42ed62e37ad30561debacdf8b7987dbcf118877c3754fdca7e266efe26ff5a4b
D4 fault_tolerant_16-bit_alu_design 7576×1077622728, 32328 337.0 kB399f3d88f767193df6a93f73fcc7d202bbc2a999a2cf22be1b2dc6a2d1472510
D5 sky130_radtol_test_chip 7576×1077630304, 32328 367.9 kB48d4e57e3e277cd9bdbaee458427c381c5efc944d57e1c637eafa7cd7cb99504
D6 two_stage_cmos_opamp 7576×1077637880, 32328 352.0 kB3edbde8c4c9461a3a795db24ed5af2a9ad692eb40e2630be87c3a694894c8337
D7 ks-guitar 7976×1077645456, 32328 331.9 kB21711e3151b7110aea622de3c22ee2093ef4e04dbbcd15a8e7fec568ebd63060
E1 ks-guitar-2s 7576×107760, 43104 327.8 kB8391fa0c1205ca98dc2d682cbb7c1c53fd1861ee3606c947671d1c5191b115b5
E2 yifive_a2 7576×107767576, 43104 308.8 kB84fbdb2d8e52cd676826645c531be4d7e2e3c3f0589ac61d4538eeddd7dc7d3d
E3 coriolis_test_soc_-_mpw4 7576×1077615152, 43104 534.3 kB14c66fdc262f591b2f752b0eb6098b8d92a4d1667428c9bbf877ed3a0fac304b
E4 sar-adc_and_analog_circuits 7576×1077622728, 43104 377.1 kBb5df1a17563c72158326de921030c75b8a95937cc18c93fb64378dc5f178b5ce
E5 junga_soc_mpw4 7576×1077630304, 43104 327.9 kBb49c36f6c5b8074b1809107857f09fa397113cb5442abc2b8fddb31dde4cfe0a
E6 ibtida-ii 7576×1077637880, 43104 327.9 kB4ddbe742c0084414ffeb766871768d680c2584087fea9b251bab08a16c6410c4
E7 azadi_ii 7976×1077645456, 43104 331.8 kB4b704598155c815f54ff554c88fa609ab9d71be759e15ad1d711da5ef70cff4f
F1 open_source_configurable_fpga_fabric 7576×111760, 53880 341.3 kB13b4ddbfb470e93d79971b66b89c2034f9baff6c57677b5ed749a5f7ef7f6bff
F2 current_starved_vco 7576×111767576, 53880 356.0 kB7bb18d0712a483ec2bf4004a6e7b3bd9e8ed35f93c0aa7200d7248f7eaa99b6a
F3 updown_counter__test_ 7576×1117615152, 53880 331.9 kB5c26ee1b1f8543d1c62c61a0abf695e8691842024fb0b83d3956f2e1272039c4
F4 skullfet 7576×1117622728, 53880 385.3 kBd8bfe4c6d728df7dd563cb094a28052fb41d7d20ab61269f8333a37df3ff376c
F5 cmos_rail-to-rail_comparator 7576×1117630304, 53880 356.1 kB1ab78958ab178a608a282f73010d2309ae20b5cb274bd9c59ab37f6fd69892a4
F6 kasirga_k0 7576×1117637880, 53880 331.8 kBa36db81a395829b62e323ea091181ea8c5d405913a15ab6ff89bd18e0ccb9774
F7 iotestvehicle 7976×1117645456, 53880 365.4 kB80e1b63899606ca42b2a2d5e1b8e2d78400f9725cd4d875eb550d80e06edbd88

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 MM5 on mpw-004

Resolved: reticle 5CS8010AC, run mpw-004, mask MM5

Mask layer
MM5 — Metal 5
Process step
162 — Metal5 mask
Drawn layer
72:20
Layer on the mask-layer GDS
59:0
Fill layer
59:28
Also
Metal 5
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.