mpw-002 / FOM Field Oxide

the FOM shapes of all of mpw-002

40 of 40 dies rendered.

90,203,856 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 (200.5 MB in total, largest 16.3 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-002_FOM_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-002_FOM_reticle.png
the stitched reticle, full resolution
125.7 MB 1bc71b6eb82a6ba7898c786d773d597bed083ed8c274f3541d92c94cf57a30f193785eef e6af9d5a0c0731be93df66c8e05708915b368bb3cc163309ab3d8e36972d219b
mpw-002_FOM_reticle_overview.png
the same image scaled to fit 4096 px
8.2 MB 40dd6eb855f851c405cc86bfd1d3ba2be33a812f45f545fb915cf906fab6c42a245cc410 72a064bd5f250f8655a24f1f642e3bd9f39c7bfb6874a248fdfddb8d2a60fc18
mpw-002_FOM_tiles.json
where every tile belongs, and its checksum
13.3 kB c26b64bb4151b187ba69ab50f029e9dbf3fccf87e4d09244f48fd84fd9974614bccfc1aa a7bbd106cca2603381e5a873698f61075249c915bd7fa381c5ae7b8fafff27de
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 axi_dma_using_spinal 7576×107760, 0 4.6 MB5a6d05ee3a974552688ebf73afbe9097530f6f1e9caed78bc7f4d44233534c96
A2 wishbone_can 7576×107767576, 0 4.6 MB852f633b14430694a94e2ff795239f07494d8940ff212eaa696ac0c0409cec7b
A3 Analog-Neural-Network 7576×1077615152, 0 3.0 MB8b4c6049a9ee98fb6e4963e3ef54be98f09432a8d66dca63678f4274badfbb40
A4 caravel_dsp2 7576×1077622728, 0 3.9 MB921024f59f4caf8b24ce120e101cef4f536b3e5cfbcc91b549446c555ad80c1e
A5 eFPGA_v3_caravel 7576×1077630304, 0 10.1 MBebb9162dcd450f2dd976a8701069cd4ecef5a6f2b5341cd8f784be9793bbb360
A6 opencryo_testchip 7576×1077637880, 0 3.0 MB720be507ef6d66c2c8225a1a37f23a9373f35f11eb58af490ed7369d5188bb0a
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 5.6 MBdd230b16dc4ff813f8cc585418be682f18a52b5daed71df6356d889e29a0caf7
B2 lexicon 7576×107767576, 10776 6.1 MBcdc1939811bd2f1773aa3c5e5f78d746eb024fa95c7ae6befb889dd63e5d7296
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 5.1 MBb712aae633f157a3cca0d67d0bdfba9ebbd6dbf3d075d3f625188929ddfd8b4c
B4 yonga-lz4_decoder 7576×1077622728, 10776 3.6 MB7556466d74fffac9984fdfd0fd9ab5bf948474e4820b278ff6d7e48ea3902b3f
B5 Potentiometric_Digit 7576×1077630304, 10776 3.2 MB8483121f2c9a023778e109a8b3c0b955b6423ee7bdd92d8fffe7682e177253f3
B6 fuserisc_ver2 7576×1077637880, 10776 9.9 MB70fde6bc2f093364740cb359d647add946f5d4a7374a0d9527ea644292feb946
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 3.3 MBf290d7020c6eb86db0dfe093bc0cbeb605b82aede844874cebe45d9f6ce61010
C1 Comparator_3v3 7576×107760, 21552 3.0 MB5f3184976747337c300a8028d4b90d9ff64f6e0fc0603643c9846fa9dd636283
C2 caravel_multi_encode 7576×107767576, 21552 3.5 MBde28cb74ec4a748e21a4ed89a4edf66bd349060941523b8ab290a4385b21c364
C3 kasirgalabs_uart 7576×1077615152, 21552 3.3 MB709911fc8f2ceb9fa38ef0766ab6c378a69ce4fe9d6b9ab5218488eeda3cdcb0
C4 Automatically_genera 7576×1077622728, 21552 3.2 MB7f419087cc920cbfd11a2be3fa68932508709925a75b99733da43333ffbfe514
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 8.7 MB3c6a3b121da45f80f17db23a273df691ac820c4122c5c98a881381e37b099b0a
C7 caravel_azadi_soc 7976×1077645456, 21552 6.5 MB94de685da4c05cfdb60c29bdf3d1e43e48bcb8e664275c79657faa5716f38138
D1 caravel_dsc 7576×107760, 32328 4.1 MB0416e392b635c1827adedfba5dacf712c95ec5ea281f0ed88279f9a279af1ce0
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 8.0 MB61a868c682398666fb791d7656539ed525d4a698366babd9e32d74607083838d
D3 caravel_user_project 7576×1077615152, 32328 3.3 MB425e3efeb6e00ec5aec3992fd6f08d8ec705f54725b6c6cc8d67a4ec56886ecb
D4 General_Purpose_Band 7576×1077622728, 32328 3.1 MBc96fc3005cf2173fc26be5cc5b139a5fde295e005235728c17c339df546f8f64
D5 zero_to_asic 7576×1077630304, 32328 4.6 MB74e426a4597fdaa23c850849f50c945159787c0b51b6d1a37f6c18de918ca751
D6 fast_gcd_for_large_i 7576×1077637880, 32328 16.3 MB873b003059877d405a4446013ed7dfbcb58829975b3b64eaf7798f226b1044cc
D7 Columbus 7976×1077645456, 32328 3.1 MBf97b88d7fa13b457a0a5bb6cdac31f5bb2b2dfb0d93ba8039d1cc65ea98ac988
E1 Space_shuttle 7576×107760, 43104 5.5 MB77236b81137d69de51f55bd26b25948c2f447cc637e04c7b0b244c7b48bba239
E2 subservient 7576×107767576, 43104 3.8 MB78ab0ccada94441ee94abf8032a7a9ef90dfc3f48fd3de78ee322e47f1cfb64e
E3 caravel_analog_fulgor 7576×1077615152, 43104 3.1 MB5a6f0d86606d66b5da68c2319aac06c792ed17bc13c113d78697438ad31e3d0f
E4 bandgap_reference_de 7576×1077622728, 43104 3.1 MB693685854a5ddc8eed3707e0c4f0097cb72b0b7694042dc2390dbeee5402b0e0
E5 renml 7576×1077630304, 43104 7.5 MB49c09b78367c8561f59688c5744a7465f9d4fe4613a45c0c5bd313d7011880aa
E6 io_expander 7576×1077637880, 43104 3.2 MB00563d795b3fad4e96932042348519d94e1de08e3c15ab816485c235aa3600ad
E7 digital_pll 7976×1077645456, 43104 3.2 MB0ed897c15fc52073d789e78dcef02d6865aa73d43a6ff7d02c71c47f8ea90e47
F1 zero_to_asic 7576×111760, 53880 4.6 MB205f94e132670099301c7f75f4036a0b2504e42e295a6c2b78aa728d2c50a962
F2 caravel_fpu 7576×111767576, 53880 3.7 MB4a9657c029a370e0c670a3499af8a0e2f49238ca32c6097859c0bbbc9b6e2a97
F3 BrqRV_EB1 7576×1117615152, 53880 10.1 MB3dcd08cbd54a2e4a1d44b9ec94937aa005dd30a1cef8cd9bf2c438912eab294e
F4 hilas 7576×1117622728, 53880 3.1 MB59327237b658992c8ca796228856a3e8eef168b32e88e584bd21d447814b3a53
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 3.0 MB4b9c0fa50bc2d97c6af749bb785873ee1d10f31dbc6d6c220be8d212c0fcca17
F6 sha1_engine 7576×1117637880, 53880 3.9 MBd0d2a19decac8ba5dd4a2b5659c010c8993aace07c55911ba36aea8ab6e64198
F7 caravan_openfasoc 7976×1117645456, 53880 5.6 MB65c5288acddeb1ffbccee1face5ea94016fab0441dded707b0defb527daa669e

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 FOM on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask FOM

Mask layer
FOM — Field Oxide
Process step
4 — Field oxide mask
Drawn layers
65:20, 65:44
Layer on the mask-layer GDS
23:0
Fill layer
23:28
Detail
FOM = diff OR tap (OR grown pnp emitter)
Used in SKY130
yes

mpw-002 — SKY130 MPW-2

Foundry product
EF MPW#2
Reticle set
5CS8007AC
Run date
2021-06-18
Submissions closed
2021-06-18
Projects
56
Fab lot
4205819
Identification
confirmed — Lot records: EF MPW#2, lot 4205819 = 'SKY130 MPW 2'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-2 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-002/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.