mpw-008 / PSDM P+ Implant

the PSDM shapes of all of mpw-008

40 of 40 dies rendered.

37,027,168 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 (82.9 MB in total, largest 5.4 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-008_PSDM_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-008_PSDM_reticle.png
the stitched reticle, full resolution
67.7 MB 02f9b3b060086f590b1747b87c6496f4e9cb7515da1834be364ff031ffac603e29e0c23d 4fb256c8afc16d599ce35ac9ad5744fe6e2217e0ea1eeaf734023ab2f305c1b9
mpw-008_PSDM_reticle_overview.png
the same image scaled to fit 4096 px
2.6 MB d1b26fbb40998a7e7e2430e6c6d1b1f4fcda22b56d80a7cef6ab7bd6c20587280b7e78ae e2d91fbaebba26f0ff6806b318d9f13b24101199429ca93b14238c3852d45dd8
mpw-008_PSDM_tiles.json
where every tile belongs, and its checksum
13.5 kB 9e4e39ecadd384032c1100f2c0908eda4b33a981641979a7f5e436eb749d8702c791e908 ca40abd3565cc945d800355ec484679f015d9cedcd7476b773f1d549967d876f
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 wishbone_pio 7576×107760, 0 2.1 MBdf585f972e38dda157d616410a5f499345e86635a0aae220502651d8bf890e41
A2 iit_indore_mm 7576×107767576, 0 1.5 MB61c0b891e711385061234d73de1819c8bae0e3b5bbf68bfaa3e72573209988b1
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 1.5 MB031642b44da69b139cf09f59bc78f704c76a386d41154454a9ef8f7341ff82a2
A4 riscduino-dcore_d4_ 7576×1077622728, 0 3.8 MBac3a92b3a023bb990b5e2d2e962c2a8c2749e3210512a1cf9875c4a17f5306b4
A5 riscduino-qcore_q3_ 7576×1077630304, 0 4.2 MB0e0bb6541ecf36d597d07bf66494b86e03f60537ebd48189b1de516ef9985716
A6 caravel_mpw8 7576×1077637880, 0 1.5 MBac10c57178b95fb8597748ebdd76350248f1b3098a4951fdc9855975db3f1760
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 1.5 MB088e0b71d2e3b98575a48b5c2c3c11caba29f28ed88dce203dd1512f01a22921
B2 sramtestvehicleg4 7576×107767576, 10776 1.4 MB43eaf9a8eaaa3cb3df0f6587883388732957cf49cfd1de222ec5e59839a33312
B3 reram_crossbar_rerun 7576×1077615152, 10776 1.5 MB172e2c0edd10e97dbcc8e2c6a5ee8da9352e542e10600c79f1e7c23cd885732e
B4 tinytapeout_skullart 7576×1077622728, 10776 2.5 MBd740bc1db5e09edaad0d03030be16a3fc7f70d28d13168934f59e1d628976049
B5 soomrv__mpw-8_ 7576×1077630304, 10776 5.3 MB6049855dff73657951d785c37f1e8317254d884af14dd8d7d997bb9e2719fb5c
B6 clock_divide_selector 7576×1077637880, 10776 1.3 MBa913e3173e07d39986b9d972f3ba017afb882a531980fe0ba48f5533b29616e2
B7 reram_module_mpw8 7976×1077645456, 10776 1.4 MB074deb9a6125d941d77d32699301e1934abb82458413aa1a7243ce35aeece108
C1 shapingthewaves_8 7576×107760, 21552 2.9 MB8e65332d01c1422c958726e7085f00951f3f6a3cb58772f7df3a77f6bdec96ab
C2 leosoc 7576×107767576, 21552 2.4 MB3245eaeb3c193ada6ed418043a2e10fd6de98cb143217642928048bcabed000e
C3 decrypt_aes128 7576×1077615152, 21552 2.3 MBda8879cba3e1f4ae4b019fde46c0f48162610305d65192c4a894d34e0699a493
C4 approximate_adder_axha 7576×1077622728, 21552 1.4 MB3c459bf50008dfc73fe9408e098cd6871d27a07fbc22574ec6fda5ab3d3dcd35
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 2.3 MB9e354e8efc1c13e5ad6a3cbc33bd11046c55af3cf2011d0de4d9c0ac3b442748
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 1.4 MB967a86c0d889c03085f5e02eb7f3869231dfbd1447b0c0482089d6c062bb4f50
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 5.4 MB5eafc314345a719b048372eec1473e29d4edf834404c82d43f0afb8fe06d7d20
D2 hyperram_controller_for_sky130 7576×107767576, 32328 1.5 MB43db8cd64fe412c3a28dc1549a8d638e2b3b7177bddf415c34aa7bfa3aaf34f8
D3 hardened_alu 7576×1077615152, 32328 1.6 MBf5c4f921abd76d4cbb656c1a47f508530ba6d7e0592a97a0d009bc14d2f55a17
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 2.6 MB808f89c09984cb55726b057ab8dcf36513938da9a80888b4ea131c767823e5af
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 1.3 MB2a140ef931f4222743cd8c4e187c167e4d0cad835639b48bf50dd19a27172b31
D6 gf180_vs_sky130 7576×1077637880, 32328 2.2 MB56e48a2737e4c142520bbe8c10d266450de6cac965c1fdfe7028df0e417744d8
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 1.5 MB006b354b08686598858d5d17d83c3c2f52317d91e9cc85235717d936be905378
E1 iiitb_sd_mpw8 7576×107760, 43104 1.5 MB75c87af36794ae161d63ddfcaae15467c6d66bf70e1ed523f5013a52655296d2
E2 iiitb_sfifo 7576×107767576, 43104 1.5 MB5ebf3d439bfab4b0696d253fec1f5200c9ccb24f241980f882f81c87705e7e95
E3 iiitb_alu 7576×1077615152, 43104 1.4 MB6b4546085c40c11b6f1f05c46ef4c71c0c6e3b0b0a9db583ff0a33d9310f0b7b
E4 ppcpu2 7576×1077622728, 43104 3.9 MBaa0dd2be2659cce86e0cffb94c6ae0ac128638a3a90c852a57f6a4ed927caab4
E5 mega4_reram 7576×1077630304, 43104 1.5 MB9aa15d2c6f0eadcb986ee18ba162faa42a2dc24279782aca3fbf202f179664d5
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 1.4 MB9748daa934ff3c0b53d88a82c3ec5503462aa051984dc42c299e34572507088c
E7 sky130cla 7976×1077645456, 43104 1.4 MBc9ae0bdd86b0e6f6f43175ae8acbdc9db2a13de5af328d6047b510d541db0f6c
F1 uetrv_esoc_v2 7576×111760, 53880 2.1 MBbbfc7ec402c263cf819d774df002cd532b89536a8435728fdb243145a2c50930
F2 sequencedetection1010 7576×111767576, 53880 1.5 MBede8ef67214c4fad73d14c58d42d62f84c338c15ab9710a19571a8b5d720f902
F3 testasynctrimux-mpw8 7576×1117615152, 53880 1.4 MBd94808e1814853d1241a2c54fdae4f0dd0240ab7bd498200bcb15fe2392aec16
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 1.4 MB6004e8fe99a01c854e1e0fe17beb4a9ea9109ba9c13248e8202c9c1fa8db3190
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 1.5 MB3264cefbbc5155acf2e63bcd4d65d71779000c70d929e46c1f6e7e812dceff17
F6 arbiter_puf 7576×1117637880, 53880 1.5 MB3bdf545315a7021f812fc816ce8a6fc3e17330ccc70fda89011654b1396ac8bb
F7 unigate-sky 7976×1117645456, 53880 2.4 MB979427eced8dc15a4f7e7622356cd2ff3383ebf6c517c3ea36ddbb30a22e1501

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 PSDM on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask PSDM

Mask layer
PSDM — P+ Implant
Process step
81 — P+ source drain implant mask
Drawn layer
94:20
Layer on the mask-layer GDS
32:0
Detail
PSDM = psdm OR grown psdm over pnp emitters
Used in SKY130
yes

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/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.