mpw-005 / PSDI PSDI

the PSDI shapes of all of mpw-005

40 of 40 dies rendered.

31,537,227 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 (70.1 MB in total, largest 3.8 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_PSDI_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_PSDI_reticle.png
the stitched reticle, full resolution
46.6 MB 1e189dc7330baaaa15b2ab395eb70feaec6900fc24f39204e7b56db8aaa2f47112b4cd7e 8c43c42c52137ff54c4cd68b54d02a4675d3ddc8d089497c1abafbe242fc6f71
mpw-005_PSDI_reticle_overview.png
the same image scaled to fit 4096 px
2.2 MB d73bb964a99925eecc4da7e5049458ef37cbfde15a3a54288a965562a277f2f7fe32a63b 31fcdd2b1db7e9627395f452e4d4ebc0470b173eec033c5bb5ca5fbc54365799
mpw-005_PSDI_tiles.json
where every tile belongs, and its checksum
13.6 kB ac685cdf8804d410cf98e3733d899af77a950e0a33e05cff7dec8d3e0ca4050b743394ab 1f21b1f1ff161e07ce1a31b1df5b45bc6241d694123048f488be95fe857ee8b0
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 1.1 MBe7e0264a59988c5c2c9d7887041161de3cf889b62826ad44dabe928c0eb50360
A2 mpw5_cache 7576×107767576, 0 1.3 MB16b6a4906978b9e6f63d8f51a43232602c7166b01544e3edb51dc592646bbc47
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 2.2 MB41f9a1b631abf135eace14ffd22103c725e14581daa3961f172585a94022a423
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 1.6 MBb1a8974a1b2f43028a9d2509b880bf60077c04a4d343818793112e8c4f65f7d4
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 1.5 MB2b639b9f850e4e980447c587c639d17b80e62f42a11c080800977ac7463cce54
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 2.0 MB05ba2c124508ef9f0e011216c5a8b3212f0d518bfd117395c628edcd761ead6f
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 1.4 MBd96502e987b0ed898c731a994593a804891c3c6499e1230c37e226b54e8911b0
B2 microwatt_mpw5 7576×107767576, 10776 3.8 MBcb460a5eb3d02309b02d41c369d1094948145c037403e68270980d76936a65a9
B3 armleo_gpio_mpw5 7576×1077615152, 10776 1.7 MB77417afd887800a5fd5b37d7054f76c8784fb3f6cd096a228acd50a44e83dd47
B4 fabulous_efpga 7576×1077622728, 10776 3.6 MB2e5e83fcc8357e6fcc8db735c49a6b602226c411f68396c35a7f5087d99f9b7d
B5 i2c_controller 7576×1077630304, 10776 1.3 MBfa6a6379f4f90a99a1e2b332d7ef85ce7ee7ad753796a5ae6222d26036a17858
B6 lbist-mbist 7576×1077637880, 10776 2.0 MB70bf4e40f5de37b55b51936c7ce38f8aaa1ec0e827b9ddc266be977db44b8e89
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 2.8 MBcc5d50a61fe6ce16fbf2246e4c5aeb8f7381f2c8aa23455f49622c6a54d829f1
C1 riscduino-qcore__q0_ 7576×107760, 21552 2.8 MB96b351af076468754c74477c052b099c229b39c670ee423b02da587554d5d672
C2 high_speed_vco 7576×107767576, 21552 1.1 MB8098f082f12193df2d7af047642901681aee21798b2342c8522096b9b58468eb
C3 openfasoc-cryo-gen 7576×1077615152, 21552 1.2 MB8949792bed3d0517c1052fde7575f800ee902281b15b370744dba562661d0801
C4 microwave_signal_generator 7576×1077622728, 21552 1.2 MB5c83d212e94ecb5e4252c72cd6f0eccf3ede7963333f3816688df2b1d7644b99
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 1.9 MBfe67dd10d8eefcefb1c1bebaf6ea5c2e93e1b5b23161679eb9fd8330c0ce3312
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 1.1 MB4e9c65023f778b8112de136a7ab93da07ec859251cdda5d31ef56d6390e08a92
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 1.1 MBc40c20f255bde98013cb2fdac1ae71d04dd9ec3ba993cf2616edde7fce0b9e02
D2 azadi_iii 7576×107767576, 32328 2.3 MB0e18071cf4baba9fa262c8a8c68ec8436145602c8763380aad30f39bd574655b
D3 tia_for_physic_experiments 7576×1077615152, 32328 1.2 MBc2b20bb663692ba5ad516d2efa29c4a19ef19553c3aebc33e506f8d27bd15700
D4 qf105 7576×1077622728, 32328 2.5 MB8b1d35d2c5e7bfb4be73d1ffaf0411284d97b10fb1ec6cb642da06a6e3789341
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 2.7 MB62f3b736d41e1fd6c8eb2dd75021ace2faa849c84d9fa28b07664452bfe9143b
D6 junga_soc_mpw5 7576×1077637880, 32328 1.7 MB8b580c327009ce4d22a1c915a9ccbc32d05be266b373bb86dc2c3b420d7f9207
D7 nand_flash_mpw-5 7976×1077645456, 32328 1.2 MBbfebde5eafe70a43c3be963d2157637dc416b9aaf493f6615c2f4b6924aa643f
E1 kasirga-c0-mpw-5 7576×107760, 43104 1.8 MB640fb6942bbbd771e0a1a524229f115bfb9340c8b9c0c08a051f402e6817eb2d
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 2.1 MB9b95bc3bf74395a10b374c2d5c79c430cb92a96acaf26c1041725a730c2f042c
E3 wirelessenergyharvesting 7576×1077615152, 43104 1.2 MBaf815628249c4681e943618b55c37ad801892c411b55832db08cccd0574236ff
E4 caravel_raster_engine 7576×1077622728, 43104 2.6 MBff40d222c8698be1264ab87026b0962a688d389a62071a2f1fd53a07f0086777
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 1.1 MBee912a88cee9fd30ad7f433630ad660a7dc1ab3a358eaeda95eecf458b106cb9
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 1.4 MBb321f91bce80cad6be5e1b9e67bf17eda0b319e4c281527559d3a10f8415abe2
E7 alu 7976×1077645456, 43104 1.3 MB9fca593825ea7855518afd40745885cdb4d59e6fbe9b7447961986db46d458a5
F1 4ft4 7576×111760, 53880 1.5 MBeb74de94a7b9382ee47db6dccc6d71b7556356ea88480c16148a58c5c95fd91f
F2 rng_chaos 7576×111767576, 53880 1.3 MB4b00ff621f2b81ac0348733226e771e027665336976df874bbc0908c98ea4fb4
F3 uetrv-ecore 7576×1117615152, 53880 1.8 MB2f6de88a5d0d265820f9cfcafd20d1c6bcd5ab67d54effd18510f91086554fba
F4 systolic_array 7576×1117622728, 53880 1.4 MBab30ae5cc845efebf1371a428d169b11f331b3665412fe3d66801e7cf2a752ed
F5 reram_test 7576×1117630304, 53880 1.1 MB2a31b0c7e781253e2e8f8300d1fa56cb0276640b98899bdb6fa33f559292deff
F6 secure_memory 7576×1117637880, 53880 1.5 MB9b9642c7dc429808b7c3d295ebbd3f8313724dd815c25d13e927a4f965476e0e
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 1.3 MBb581d97d00ed95e131f3f2d847ad36fd1afae3966ec741f3dba2040177d5990d

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 PSDI on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask PSDI

Mask layer
PSDI — PSDI
Process step
82 — PSDI
Drawn layer
94:20
Detail
second P+ implant, same drawn layer as PSDM

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.