mpw-002 / NSDM N+ Implant

the NSDM shapes of all of mpw-002

40 of 40 dies rendered.

41,457,778 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 (57.9 MB in total, largest 4.2 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-002_NSDM_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_NSDM_reticle.png
the stitched reticle, full resolution
40.4 MB e91dcf5c53568b85c16cec8fa67512e854ec8cdbfd52fe68c085de0e405b090b695180ce fd05e3ebf49b0e7f7d9c5c48d2dbcb422e443454e94868138de3a41c7a887ed8
mpw-002_NSDM_reticle_overview.png
the same image scaled to fit 4096 px
2.0 MB 951b40a6f602ce596e982da0f1f11e0d055ed731f43c68a0d7a6c5642c0f02d7f4962683 4274c0b2238f8759c77ef5e759f7eb23f26dc08ff5e7b5c7a2af0dab1dfebb58
mpw-002_NSDM_tiles.json
where every tile belongs, and its checksum
13.3 kB 6a5aa13c44c3f2349029f4358b3149cf144e71b270378277f1b55401e836c392ddad2ed5 db274102ab72676031ac6da210d6731bf5d2d3eb073bce7207052c013a8da69b
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 1.2 MB3e485a03ed50b203d349eb25fa9e25d12d45df3d469fb5d3d66403a8b41b391d
A2 wishbone_can 7576×107767576, 0 1.4 MBca51f5c11d381401383896354017722d14590fdb5a404513fd9db33372f1003e
A3 Analog-Neural-Network 7576×1077615152, 0 872.5 kB019246aee7b5938bf0c6389b0196267611426e3d4035e77409b5162f0467f076
A4 caravel_dsp2 7576×1077622728, 0 1.1 MB4f181bfaf292f5ebadfa9ca767809a1733932d71eed37c417435175141596aea
A5 eFPGA_v3_caravel 7576×1077630304, 0 2.7 MB991ce1885412210ea3777f5359ee40f04961e2fba4659c68c1e2f054b31423f5
A6 opencryo_testchip 7576×1077637880, 0 875.4 kB636bebfcc975303f0e5562b0c4706bcc388198d379a50e87c82a49b4d9293ef8
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 1.8 MB6db653a06903c2e0550fbce471eae0f3f8e68ab5d0b7877834c82e38dbcb5a3b
B2 lexicon 7576×107767576, 10776 1.9 MB3045c01e7aa0731429f0a6ae01cd59c2b2508427bdccbe7ec04b84e104a405d5
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 1.8 MBb59bec30b0043878d8a827ba544bcd82f6dcd5ef66b5798a3f5ab5de278da9f3
B4 yonga-lz4_decoder 7576×1077622728, 10776 1.1 MB748e710ccf350fccfc3c9770595be878f7af0dfef35ac8008ac1fd449d8b2996
B5 Potentiometric_Digit 7576×1077630304, 10776 937.1 kB24ce83ddefd1dd09fa1723b1b99ffd3d428ca7c2e5a0bd5eb7d77e0617ae3844
B6 fuserisc_ver2 7576×1077637880, 10776 2.6 MBe08b627e42c2bfe6b4d7fecb2db21dc07b68ce90210d1af94498f60b7539741c
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 895.9 kBf7238c48dc30e7def14f62b089925e031885d0a1c36195700e222bf970ed4cbf
C1 Comparator_3v3 7576×107760, 21552 875.4 kB9b63a393354425ef4fba0296068820d109a5d17d594be7c1452aa9ec924f2f2e
C2 caravel_multi_encode 7576×107767576, 21552 1.1 MB337676e65bff5bada41d7f83b6895c4cb9fc652247c6b72b91d9f4fa782f3139
C3 kasirgalabs_uart 7576×1077615152, 21552 985.3 kBdee2a59aa44dd816dd2a56ffca9f0655fda34f8be942d4fa21393f3467a653d3
C4 Automatically_genera 7576×1077622728, 21552 928.9 kB5c656c943dc43a48ccd481202e0ebceea88e70d59ae371e49adb7c727b856af6
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 2.6 MB09d2b35efd53d0a853ca04f6266bb446dc60493104aa54fef42e1527f3c41a62
C7 caravel_azadi_soc 7976×1077645456, 21552 2.0 MB60f9f3d77b5261feeb04aaec4b0349e9cf22465666be0df8dad0931c00b01c2d
D1 caravel_dsc 7576×107760, 32328 1.2 MBdd57204a7895468be07a0d7f1ac612240eee1f374a4a757204e483f855e007c6
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 2.3 MB30f98c83b8179d130fb852c3d99c704fb17384fbd3f1594dbb69a2fbe7c7ea96
D3 caravel_user_project 7576×1077615152, 32328 990.5 kB4d8227a93b506a70b2e904fec528f81d8034b93ac9c02cbcb6ad8e57f00e26cf
D4 General_Purpose_Band 7576×1077622728, 32328 906.5 kB3edef19a7ad4302ad8b099ed2e8af24b9a9fec9f8997fb2468e3de9635f63dd4
D5 zero_to_asic 7576×1077630304, 32328 1.3 MBb86734733012dec4f1b0202a98c962406ce195e97e66235c106c8a8430bb4a57
D6 fast_gcd_for_large_i 7576×1077637880, 32328 4.2 MB5a9231cccb1539b10bb19a6037b6b3502600aab43f3dff4da7d27edccf7f0e3a
D7 Columbus 7976×1077645456, 32328 870.7 kB1d3a7871d63e45270a7967a1f69eb19c37999d857d2c8beaa11b5482b4be9a65
E1 Space_shuttle 7576×107760, 43104 1.6 MBf09e6804c5c3d2e05cc4efdefde2d8e455b23b33f8b6d65f4d607f3a693d71eb
E2 subservient 7576×107767576, 43104 1.1 MBace28ef7a66e92eea06fd223815365958abd0efe25c42003b97289e1a5742d23
E3 caravel_analog_fulgor 7576×1077615152, 43104 872.4 kB91ded0c73cc116af378ea8667cdc73eb0dd4fd38df2b0ee738d6ce1e67540251
E4 bandgap_reference_de 7576×1077622728, 43104 906.5 kB37e266ff4e9f1255adf95cf14a3b2e927d3f3eb07288ff9f709ee236b1350dc3
E5 renml 7576×1077630304, 43104 2.1 MB93fb653ba035f7c24a3ea5099d266145a9f0af723eefe81610cca85d66947a08
E6 io_expander 7576×1077637880, 43104 913.2 kB93654ab5f7d4d5ab1f39532289dff5c75ead1ba57332bcd9a047d75aa347281f
E7 digital_pll 7976×1077645456, 43104 928.5 kB31383a8305da42186f080b47f4a9390ae5a84519ff3e40c9adb86b0d04ae399d
F1 zero_to_asic 7576×111760, 53880 1.3 MB476750789b45fefef1f22c5615ce448024ecebc7cc1db966a57aee8c5298cdf7
F2 caravel_fpu 7576×111767576, 53880 1.1 MB2f4caa6fcef43b57c5910989a5009c196acff1d49afd8926f0403866f0493dd7
F3 BrqRV_EB1 7576×1117615152, 53880 3.0 MB807f93298bd1bb17b3c95c2d01b2dfe44530d2c8e75a822cd53e31eced6726c4
F4 hilas 7576×1117622728, 53880 879.0 kB0943a26d313e4efb9375b463d0d7c78ad69c7bd14defc7e7cf6ae2884480aeac
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 879.5 kB8ba5da7c2a0cd043adbe7645fefb0c858224870b984ffe265fe5b6eec7ab983b
F6 sha1_engine 7576×1117637880, 53880 1.1 MBf17bf6aad34dbc5d2b2a5d0f3603e5a1ecdb4c3931c983028259f4dbf3cd78b0
F7 caravan_openfasoc 7976×1117645456, 53880 1.7 MB3710b53c5cbac8890d86e9c2af379e8d3ea6e189d8d7c2fffae158361c9c2f10

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 NSDM on mpw-002

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

Mask layer
NSDM — N+ Implant
Process step
85 — N+ source drain implant mask
Drawn layer
93:44
Layer on the mask-layer GDS
30:0
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.