mpw-008 / DNM Deep N-Well

the DNM shapes of all of mpw-008

40 of 40 dies rendered.

20,792 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 (4.7 MB in total, largest 120.4 kB), 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_DNM_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_DNM_reticle.png
the stitched reticle, full resolution
3.7 MB 6faeae3fe45752d78cbc68c3903192749e8a405ce5a2488016c80d2728fc0b161c06a441 4626e8d10cefa087cdb9f5b3b5c6ae851cc9af3eca73ff8e293a1649aee3a808
mpw-008_DNM_reticle_overview.png
the same image scaled to fit 4096 px
188.4 kB caa23e91741a167f30cba1c4235cdaddaf3152fbb64a450e571569d0dc27d90c2229977d 64293441e1731e1794c5abddb4ea89359eda547ff0b0e249c81ae4e912467bde
mpw-008_DNM_tiles.json
where every tile belongs, and its checksum
13.4 kB ed0ee41819134c9a6eeabaea255d74fbec052472f55e3bb0cbe77ee2820250c0c1dd6536 236a82904259e342f29e037f276da66b9b83375e892a3aff0540de516c4c6e91
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, 0 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
A2 iit_indore_mm 7576×107767576, 0 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
A4 riscduino-dcore_d4_ 7576×1077622728, 0 112.5 kB3ce3c51e59a1c06ab697c7e03edcdf850f604c8cff2d7a9d5cbb82b4c8e6009c
A5 riscduino-qcore_q3_ 7576×1077630304, 0 112.5 kB3ce3c51e59a1c06ab697c7e03edcdf850f604c8cff2d7a9d5cbb82b4c8e6009c
A6 caravel_mpw8 7576×1077637880, 0 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 113.5 kB14ab02ffcd2caf28356cc396a9e0c0c0e8df18c8d8d85b95e2d9d1529f7bc4bf
B2 sramtestvehicleg4 7576×107767576, 10776 111.4 kB23348fb20943bcaa3695e06365748802690992ff4eb3b4ecebd981daffdd8f3d
B3 reram_crossbar_rerun 7576×1077615152, 10776 111.4 kB23348fb20943bcaa3695e06365748802690992ff4eb3b4ecebd981daffdd8f3d
B4 tinytapeout_skullart 7576×1077622728, 10776 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
B5 soomrv__mpw-8_ 7576×1077630304, 10776 112.4 kB5601923c9ca6378e918c690f579e970faaf40ae86c2492a6f0cb94c700834026
B6 clock_divide_selector 7576×1077637880, 10776 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
B7 reram_module_mpw8 7976×1077645456, 10776 115.5 kB559fb2e40c49b63efb3a554480fc638b3fc2a035d8fb0f4ce88ecf60e06e67c2
C1 shapingthewaves_8 7576×107760, 21552 112.8 kB9f5e458ea657e6579ce46cb6134a744db25d6140258aaa098ce7499096e69a67
C2 leosoc 7576×107767576, 21552 112.6 kB08f109ed36870fb8c52402e766de06b87369c5ffe1f3ce89f577c9bfc569ee3c
C3 decrypt_aes128 7576×1077615152, 21552 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
C4 approximate_adder_axha 7576×1077622728, 21552 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 112.6 kB2d9ccb7d120e69685207104a49d644fbe5599666a85cfb3599b969c71dc1be3b
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 115.5 kB559fb2e40c49b63efb3a554480fc638b3fc2a035d8fb0f4ce88ecf60e06e67c2
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
D2 hyperram_controller_for_sky130 7576×107767576, 32328 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
D3 hardened_alu 7576×1077615152, 32328 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 112.8 kB2a4d085d3f53da26d399111df24786488a4cae0106d90b98b4c3e7e84e930d50
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 111.4 kB23348fb20943bcaa3695e06365748802690992ff4eb3b4ecebd981daffdd8f3d
D6 gf180_vs_sky130 7576×1077637880, 32328 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 116.4 kB91ed4fda16a2fc3babb16324fd02d00989912def40f6db0febd7e6ecf0ebe6af
E1 iiitb_sd_mpw8 7576×107760, 43104 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
E2 iiitb_sfifo 7576×107767576, 43104 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
E3 iiitb_alu 7576×1077615152, 43104 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
E4 ppcpu2 7576×1077622728, 43104 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
E5 mega4_reram 7576×1077630304, 43104 111.4 kB23348fb20943bcaa3695e06365748802690992ff4eb3b4ecebd981daffdd8f3d
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 112.4 kBd33c8014c5fdbadbb3282c9ff7ebcddd13e6456753cc8cb760ab2a8e9508d49d
E7 sky130cla 7976×1077645456, 43104 116.4 kB91ed4fda16a2fc3babb16324fd02d00989912def40f6db0febd7e6ecf0ebe6af
F1 uetrv_esoc_v2 7576×111760, 53880 116.3 kB02b599d577bdc6c7383ba0e1dbcb64f5e6283de0e0040c78921faaf290ef77a3
F2 sequencedetection1010 7576×111767576, 53880 116.2 kBff6116af427f2e7e46f42361e94359f4c9ed8cdd9f098dc9750a0720f9159904
F3 testasynctrimux-mpw8 7576×1117615152, 53880 116.2 kBff6116af427f2e7e46f42361e94359f4c9ed8cdd9f098dc9750a0720f9159904
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 116.2 kBff6116af427f2e7e46f42361e94359f4c9ed8cdd9f098dc9750a0720f9159904
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 116.2 kBff6116af427f2e7e46f42361e94359f4c9ed8cdd9f098dc9750a0720f9159904
F6 arbiter_puf 7576×1117637880, 53880 116.2 kBff6116af427f2e7e46f42361e94359f4c9ed8cdd9f098dc9750a0720f9159904
F7 unigate-sky 7976×1117645456, 53880 120.4 kBb1ceae9b8122c0cb057b22c98da91a79821777268d6ef793ade0f34d2c2a7871

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 DNM on mpw-008

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

Mask layer
DNM — Deep N-Well
Process step
7 — Deep N-well mask
Drawn layer
64:18
Layer on the mask-layer GDS
48:0
Detail
DNM = dnwell sized by cdnm.3
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.