mpw-005 / FOM Field Oxide

the FOM shapes of all of mpw-005

40 of 40 dies rendered.

82,403,356 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 (175.0 MB in total, largest 9.2 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-005_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-005_FOM_reticle.png
the stitched reticle, full resolution
104.5 MB bdd35592990fe1401c15528693b39a9d692620251f31abb50d46c7ba0a923d773cffa4c6 8eac0db34443ed76abf67c85fbd5e90bcbd13d4938452a62e8d6b77b8abd764c
mpw-005_FOM_reticle_overview.png
the same image scaled to fit 4096 px
8.0 MB f37bde40a72958dcfd1738b1e1d07136ebaa8e456033cbf52a43c934d8d98969bfa3b992 598879ab928d1c77d62c54720cb503392dcf38a300f42e9db00143f8d36d28e4
mpw-005_FOM_tiles.json
where every tile belongs, and its checksum
13.5 kB 1a0533ac300b28fbb84a763604210ca4388bfd7f0fb77a656c7fe4cd0370d9301652574c 5c2490cb3249122e34aa31f4334213da607a3d7a0a9bcc2ae4cf264b13866fa2
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 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 3.0 MBa2e8a81c10123293b174b9d1a33ca05314a671d3cf6a954fd670c5e948445ecc
A2 mpw5_cache 7576×107767576, 0 3.3 MB00992ac07de5d41f526589c32cbdcdfc8b37e5aae116b939017782ede48270b9
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 5.4 MBf30a5beebbbc646fe21bfde0c1d3c180da4c27ac5cd7382fd358355793bd7599
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 4.2 MB08502b282c1b5fe8876936ccc17a8ee1c7f506681fcf14925b1a75fd69880dbb
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 4.0 MBebc11526afd5c3b9033ab6a3313efd390ae8949321b4307df09c67461af445c4
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 5.0 MBed802b23e6a95be9dd942359f6827e305144a0f30247f79ea2d368a1e9010d74
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 3.4 MB7cbf22929a01d26e16f4aa7526116aedf463a0c854be44f3453323769f780a29
B2 microwatt_mpw5 7576×107767576, 10776 9.2 MB3edf718abf731f8c673f70aea4be22c82051c16483c3c62779974265a2b27890
B3 armleo_gpio_mpw5 7576×1077615152, 10776 4.1 MB59ddc0d3c771fe5fa7492f5dcda940b4e898909c144c88011a4d14cd5b1c80f0
B4 fabulous_efpga 7576×1077622728, 10776 8.3 MBf3759a117a80b5793120c5d838691ca539eee06daa6c4ac9ebf352b382a5c897
B5 i2c_controller 7576×1077630304, 10776 3.2 MB737eec1c995cb0beba6d6db9af988c9e3907ab33cb4ded29f04f8db7941fac19
B6 lbist-mbist 7576×1077637880, 10776 5.0 MBcfb1f4e83fd38b55661c4f5fb76042c017b3911fa71056e040c06cdd11b2028f
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 6.9 MBdfe4c799f8ea097241117596c828a591c3a27b0f071d23654017eb3f644633b5
C1 riscduino-qcore__q0_ 7576×107760, 21552 6.8 MB385e5d6961d17698c3e4a66885a95c1ef1eabe63d092c97291eefb93ccaa8e31
C2 high_speed_vco 7576×107767576, 21552 3.0 MBa032d1fb2a671daedf46ff8e210ede2c17665d241f7bf0ad20a9e15be9fa4e78
C3 openfasoc-cryo-gen 7576×1077615152, 21552 3.2 MBb577ada1db4838e37ddd3c97d77496baefe17b3483138734888ec2e06e380828
C4 microwave_signal_generator 7576×1077622728, 21552 3.2 MB8164e87c1248b2b7146061e347994cb599530d3d6ba891156c716fff3b8092b3
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 5.0 MBdc1c737110c40908e0b26b2ba2afbad8c5505e6bf710f49b2e8502962779522c
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 3.0 MB3db047a01d2cde67e68a49bdbc39ec89a738de2ba3702444f609728e79a235b6
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 3.0 MB9a051909eb5a412931215c4e7a4e4dd69658b17514488006a0bd9dbefdcb6a5f
D2 azadi_iii 7576×107767576, 32328 5.8 MBd84a6e372b4cff63010c84be7732882a9e648d5497b43ee96324e8f5bbae9087
D3 tia_for_physic_experiments 7576×1077615152, 32328 3.0 MB0a83d50ae661103067ab709831db73e61f4fd13658a8e64baf74d499ad3a4659
D4 qf105 7576×1077622728, 32328 6.0 MB6f013fac5ff0022915eb9419f58d8c4bea789f094abfb569b8f046696c00ebac
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 5.1 MBbd6350033b5844e686aa22d821bae910eeafbe19e7774a8ab7f1588e0c64063e
D6 junga_soc_mpw5 7576×1077637880, 32328 4.4 MB95ce2dc7e65e33f639a052d949c5f8d7ae94bb6ead8f5f38aded592e2a2b4a37
D7 nand_flash_mpw-5 7976×1077645456, 32328 3.1 MB36cfe133c8051c2ff9995a7c22dcb2fcaf109e9aba1e8900f7fe162eda605f96
E1 kasirga-c0-mpw-5 7576×107760, 43104 4.5 MB3f2958caa654475cd1b3755ebe52bc19315eca0031b4255ec6f7dec2482e2cc5
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 5.7 MB1f86c36b53966bba13bc24b95227e22af21fb7003019cb996cb91a8f7e536c27
E3 wirelessenergyharvesting 7576×1077615152, 43104 3.0 MB31a745362d5ccd48f624e3e0e7a62dc687dae9516bd7e43a237be304e18495fa
E4 caravel_raster_engine 7576×1077622728, 43104 6.7 MB7d6684eb68f64dd5133d3e5d2a72dcf4567fba8cc56a17d4ed15f23095abc220
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 3.0 MB7b64707652add56dba3f897a539af3d328974c42e46d6de1dbdfa392760d1c5a
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 3.5 MB348d45c763c3dcb8451cde2748c2ac04dc4760e34394d35f2fb3f09612b4d715
E7 alu 7976×1077645456, 43104 3.2 MB92199f2bf20dc508bf2ff4c52b706496d8f1bfca2d32968f3dfb8bae024344ef
F1 4ft4 7576×111760, 53880 4.0 MBbbcb3284a840e563f99325f437875b46c0ff8bf4b2168795354deb485609b6a6
F2 rng_chaos 7576×111767576, 53880 3.2 MB81d25bac4ec626bee0c2bc2235132f96e552121b5509b807faad86ec3684f772
F3 uetrv-ecore 7576×1117615152, 53880 4.7 MB3a015d852f0df466eb7b38fcc8c60bd633e5fe010f25af4f6991e9370160b589
F4 systolic_array 7576×1117622728, 53880 3.5 MB23115950975bbfe8118dd32f6c4a92bdacfd90c1adcbe15fcc4eef932b7d5f63
F5 reram_test 7576×1117630304, 53880 3.0 MB28724a2161ee716f1d34ced1595628303d3cbc0703e451f385fc9c10b34a63b9
F6 secure_memory 7576×1117637880, 53880 3.5 MBdb43a69b153612c9abba76117463b39929f67bc4d94f1daa95dd5f23e543fd3e
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 3.4 MB9c918050a513bd179fda3da32444b3f2b3f93055de42a9cde30f4f3393fd0cd1

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-005

Resolved: reticle 5CS8011AC, run mpw-005, 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-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/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.