mpw-007 / FOM Field Oxide

the FOM shapes of all of mpw-007

40 of 40 dies rendered.

97,930,359 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 (240.6 MB in total, largest 13.4 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-007_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-007_FOM_reticle.png
the stitched reticle, full resolution
190.2 MB c1657a68b5554b7197a855a1149c543e9cd37d8f17ba85534069b9cf9d539c80688e3339 5cf51885b269c7090531301fc5b5f5f334858b18aa4f16e09fb2ec14b35d3e86
mpw-007_FOM_reticle_overview.png
the same image scaled to fit 4096 px
8.4 MB f8224123cbad409c8fc59346643a9eaaa1239f346719419dea4f746b1b4a1512bb6d5ca9 c3fb8875332010a82c823300612a9521bace5df05cc62ccc53a24c7f2914311d
mpw-007_FOM_tiles.json
where every tile belongs, and its checksum
13.5 kB 9ac2303c8df67c42e6bfd9357360603ead585368d8cdef27ff310f6c45b1abf4b79c1c17 96874fa8a61178d31f7473e316b1d8977620ced4455d9d1e6a61f5484b0dced3
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 tinytapeout 7576×107760, 0 4.6 MBba22a4dcb06ac079b53e7696971b07577f53120afa766d3477a3ed07a9186c58
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 6.1 MB43dcff72a08a934e6057d6824c6e00a6e7d5a8f631dbe47926e38781080136b7
A3 microwatt_mpw7 7576×1077615152, 0 9.0 MBed14444d6a23f36e3335ca8f04be6cba846261c4bc708aa43bdb640877bbec4c
A4 yonga-can_controller 7576×1077622728, 0 3.9 MB3b45c394423532d613e4ed54f395850b96ed0c433fc511b8e94aec633ba41878
A5 rocketalpha 7576×1077630304, 0 9.3 MB45220267a4571333da76550b2e78c5d6a45f8918d90b97fe369bfb104659e211
A6 riscduino-dcore_d3_3 7576×1077637880, 0 10.0 MB40807e664763e217e2f31b88269b3d71287ea29e2f838bee7872bf4484015f52
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 9.8 MB14bff05c1747386eaff3ba82a75e96f4ad4602fea005d57a142696e46c27e3da
B2 riscduino-score-s4-3 7576×107767576, 10776 9.4 MBb1e1a64a716840c909f5af993ab64d800751fa12513b9bc3522cdadad2921354
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 3.1 MB3e054f9a6cb92a0d5d1ed265388b3b417fed30a73e8b201534ee2f4458e6b845
B4 sramtestvehicleg3 7576×1077622728, 10776 3.7 MBfbd4f0c169962f0dd01d218376821a589bb797b54efa8b259ea851e5446b27c1
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 3.6 MB9ed16fb9250b040afab3b969f324ed7ee8c498b68cd10152ecdb6b78ad935ef2
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 4.0 MB3755ca61e9ed707a376e935287f3298d9d515f9e7487535cb2500efd6c64541e
B7 reram_crossbar 7976×1077645456, 10776 3.8 MBf61b8dfc106c46b3f2a7ff5b0c9affac57b0e4deedd4811092260a843b3f2c72
C1 trainable-nn 7576×107760, 21552 7.0 MBc4e2abf000dd4b91f3ec04f559ab9cfa0fb9c47ccf267b7bc60becc70a6a6089
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 3.6 MBd8439ed177ac668a277c3c521592f011498f8f0f2a24d4b0bcec39a2c27b604c
C3 bitcoin_mining_asics 7576×1077615152, 21552 4.1 MBfd9213c917894f79f87ed0ee96d5767519ffa93f52fa7deedf12e20b25b142ab
C4 yonga-mcu 7576×1077622728, 21552 7.2 MBc713479576d1b789219bef9bd25064bf1508b49a6d4c669af9afbca514f863c0
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 13.4 MB1457fc9c1d39a4d9d4726a7aeeaef34295d311cee89c7d7782126e876a8e10a8
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 8.8 MB040f32b3df479f1541c4431690c53656746693873bc9ca58635e8c6ffb81adc0
D1 mixed_signal_circuits-jun13 7576×107760, 32328 3.5 MBc437f0848fe17a68f6f8be29debf68baaa37c6f87605caf6c652f0d0e9dde92d
D2 fpga_programming_management_unit 7576×107767576, 32328 6.2 MBc79354c908eace7bfedac663217a6de19bf6a4fdcf3be7b9e7e37fa7500ece6c
D3 warp-v 7576×1077615152, 32328 4.5 MB943d23c9abbf301e8dfb222fd7eaa21bb78878a47588e2040320d381c776cc9c
D4 leaf__mpw7_ 7576×1077622728, 32328 5.2 MB793d7676dbfa0339a9e9bebaf763cf37ce3baeafb4f2ebf5f810c3724d4e56b1
D5 in_memory_computing_sram 7576×1077630304, 32328 4.2 MB009524b62350c37b45ad9caacb79506535ea3adb8bc70bb92b34bd41200fd13e
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 3.8 MB221d081db93ec5ea0feb54dd5918ff3f8e9c282d40f24dc727732e065bd7ea7d
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 3.6 MB065b719620dca5a166501bcdc1b566db36b4a7e765893dd79a20a992d14180a0
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 4.0 MB34e5a92702577de81cbce4b5358ef67cd5df5b1114262fef421ab66a4ac378cd
E2 in_memory_computing_rram 7576×107767576, 43104 4.4 MBa0e114a8c9bad9330e5b5078b6039f241da9d5f63340d59bd07563513bba2a3a
E3 rram_imc_v2_flat 7576×1077615152, 43104 4.4 MBa0e114a8c9bad9330e5b5078b6039f241da9d5f63340d59bd07563513bba2a3a
E4 topmetalse-dps 7576×1077622728, 43104 3.5 MB1801f52109d6673587e7507d526c5f03fcc276e7ed7e10614fa9d21203163dde
E5 chaos_automaton 7576×1077630304, 43104 12.7 MBc6f317c5b7197d65fde400fd5076232e2786010e3fbe87b948a2dc2e67ec9927
E6 hehecore_resubmission 7576×1077637880, 43104 7.0 MB787f78314dcf82b67692110f313fda2512d36d30f60c282752b3e9e55a2f1e34
E7 waveform_generator 7976×1077645456, 43104 4.4 MBeadce936fd1af2c16d1d3e8a50cc5b0dbb1b4bd2de59e3ed02f424c7442cf26e
F1 rift2core 7576×111760, 53880 4.4 MBc25e3a0594d45941d08c2e0cda3d62f342f83fef702136fde2b697ed3b318b5b
F2 rift2go_2300 7576×111767576, 53880 12.5 MBeb5ca01714a8b06be0bedccb01e8c4fbff7e6571e24a9f45824ddb7cc2e097b7
F3 sloci_resubmission 7576×1117615152, 53880 3.6 MB76a5199e522f0979287ca5ae12ed5ccba1e9ebe138be6551e4187a1bc8dfd6c5
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 3.8 MBbb62c69547cc82988b96c3e35d5a23addd05f23cf49b7054cc2807c71066ab84
F5 crypto_aes128 7576×1117630304, 53880 5.7 MB62264d64d080bb37bc85cfa402af03cf1df21fc717b88d35bd6b41f3ff32072b
F6 graphics_controller 7576×1117637880, 53880 5.3 MB580e6dfd3f988cf9a8055f79a2eb11153cf866040af75d54551fd10433d251e4
F7 prga-test 7976×1117645456, 53880 9.5 MBbe022ab9f11dabb9d7a813390b7dd62dd4e5b35d40c204378f6d516e07099ef8

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-007

Resolved: reticle 5CS8016AC, run mpw-007, 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-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/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.