mpw-005 / NWM N-Well*

the NWM shapes of all of mpw-005

40 of 40 dies rendered.

26,260,556 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 (21.1 MB in total, largest 963.9 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-005_NWM_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_NWM_reticle.png
the stitched reticle, full resolution
14.3 MB 72f584a32447e21d2a24430c3f41ccb40a93ed0e993b0dc211494b56fe8e23991e2bd232 2303ec30fd8230322a27224c5ea6faeb6c60df8b9ae28ddc2180574f0b089cb4
mpw-005_NWM_reticle_overview.png
the same image scaled to fit 4096 px
1.5 MB 1d020c82fd412916de7e63123f23624bb4eb962665f21d941acf8fac55b1cb2d92e30027 e3985814a8bbb9cd9c54f220251e494f8709f2b953948723b688a1ecda17d5ab
mpw-005_NWM_tiles.json
where every tile belongs, and its checksum
13.5 kB a9889ed6784f748285136ee2ff6aec2a6b46e3e389cf2872997ee8f7b45865e9dc70a839 82ab73cddf845bb17f8878330e27de9b910da87c4e4f209b7daed8c922d50178
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, 0 393.5 kBca2c9508a7cbe7ab276011d0bf02e575429b5e0a6fad23afadacfdbc301ec0fd
A2 mpw5_cache 7576×107767576, 0 416.2 kBefcb34e20ecfd39a4b9dd9d6bff93da5f91ccac5f3f3dc5f8a4be7247b59ad34
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 657.3 kBed92f69c67f7c2d88728bbff3b6c2076dec238b34db4741f699c1c3f03e6adba
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 510.9 kBeac240c85e795f4f90d7e8d41365794ea583566cc0fb4878f9b4007ef71840ee
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 494.4 kBd777b4c450a7855e38c7af70d024f38829b293944bac577d6f9e9a667b91c998
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 597.3 kBc6d1aec429e349658e8a20296398a044c91d083c8028fe257cbd2c79a594c0a2
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 421.9 kBd1d58de8058bc57be2133d7aca894c176cb3d70c9e904cb930781d0678ed1828
B2 microwatt_mpw5 7576×107767576, 10776 963.9 kBbf00d9e287276bd74da7b2d0251c09ded582bbdbc64cc7a3772598e108fecea3
B3 armleo_gpio_mpw5 7576×1077615152, 10776 484.9 kBaabe131a21f50cb76e40452a58f2f17191fb435f52e104dc8a6eeda6e223903b
B4 fabulous_efpga 7576×1077622728, 10776 881.5 kBf78d60a17df7130cad1d5dcacf62fa5bfde5938a21b7c87dffce7507a18d1d0c
B5 i2c_controller 7576×1077630304, 10776 432.2 kB88a7fe765b4aa1acd74437b624c6490855add55eff710f70efecd3b046be475d
B6 lbist-mbist 7576×1077637880, 10776 585.6 kB692680687fb06a2f962cbd83a1ddda42cad245bf9811291b010257b2d9609b92
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 749.8 kB2eb0f371c4bc4d4de7158ed892f97d4cc971aa505c29bb149fc31f4410228c52
C1 riscduino-qcore__q0_ 7576×107760, 21552 744.1 kBe6442dc8739ed3b23c4aa080f438bfd061e7bebc8b2f162b0a7e2e2dd702c585
C2 high_speed_vco 7576×107767576, 21552 392.5 kB57e679d3dbb22dac565bda0daf88bb450c908cf343465b05adfad0343638c0a2
C3 openfasoc-cryo-gen 7576×1077615152, 21552 401.0 kBe4e8c851ed3528f2452a8c4ed64f1b2e329277bdb0aee5dbb83b607efe208164
C4 microwave_signal_generator 7576×1077622728, 21552 408.3 kBa7873d91bbd7bd84e5b75c9c43cbebd676e903866d57a783520c15ab3d45f149
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 613.8 kBf6c78661d617d4e9a1db24fa920e5809c09f6599b8caa2c8360b216d1ac68c10
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 396.4 kB13b8a6ec5165cf32919dc0977c862713cf061c3ef62e9f57a8716cdf986b006a
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 405.7 kBc2d058f4ec8fabd978770776e0fa62645c27d10a9002701e8bd2338896190307
D2 azadi_iii 7576×107767576, 32328 727.1 kB1af5902daeecdbc6739931b8e156a99c0586826705220e2a6e04c269697bfbd2
D3 tia_for_physic_experiments 7576×1077615152, 32328 388.7 kB239a0398e0c6403aeafeac49faded184783f7c5f6dc8c28f43a519ab3b907ca8
D4 qf105 7576×1077622728, 32328 665.5 kB41d4f3d3d314c316b1caa865193b2c20ad7dc0e96b0a4b7727d6439da188fd6f
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 420.3 kB3439592bd52107fb02b7e028cc9a70b50c3b7232c141003105b2340cb17a5e58
D6 junga_soc_mpw5 7576×1077637880, 32328 518.3 kB3c8401e705672e2a73f2672000854a3f259420d25eb9dbc4399d87ef63f78c03
D7 nand_flash_mpw-5 7976×1077645456, 32328 424.3 kB145af9d382c409b7c4ed9f2b6e3d1cd2c6ceb9f521ebd982115a5c4962ad9fce
E1 kasirga-c0-mpw-5 7576×107760, 43104 549.7 kB15b0f9b43c596e4ca0b51a3485864c58f36cedd60cd99ca1ee687d2c5dc16b80
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 718.9 kBd2f3c689ff5befc8c280bedcc13d35be117ff170a6c26208d28a0d3a932b8c38
E3 wirelessenergyharvesting 7576×1077615152, 43104 389.9 kB65cda7269c4bd6ddd24f79a98d5a77041ce4c43c372893c3f8fa0ea5cbd7cf03
E4 caravel_raster_engine 7576×1077622728, 43104 755.9 kB94dfb5cb34769925f32362191362e59c87b6840d7f36f18c6ee435dc09f0bff3
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 396.0 kBa61f1cc553cd130c92a404ba97689085b4f4de06cc49c25cc4ed474e57f42387
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 433.1 kB3ab3022a4ec67100cd3f4d44c98e7536493ee21b09150f10eb01473b4552f94f
E7 alu 7976×1077645456, 43104 411.7 kBd8350a16d584982389f37fbeea7c1cab0dc93235353fc9f74867d70e83e7e066
F1 4ft4 7576×111760, 53880 485.4 kBece21f48533d89149d9ed2022c1f865769902adafca06b3c7e780f649a521f8d
F2 rng_chaos 7576×111767576, 53880 420.5 kBe00b07eec5155e1d0d25f8a6465f824a0ac9c74eb87ca30bc61a1ca78b5144d4
F3 uetrv-ecore 7576×1117615152, 53880 501.3 kB90a2a880748ff0337893aee9196fd548affafaf65a2b1da727ce8b468cf8b493
F4 systolic_array 7576×1117622728, 53880 416.6 kBce694c3e0ffe4ff01079713e0fa20ab384773760f8a5e796f9298f4da40de11f
F5 reram_test 7576×1117630304, 53880 395.0 kB9be7629afa1cd1123c7aca1aaead6d0b86aaba10c28add0284e32767240d863a
F6 secure_memory 7576×1117637880, 53880 453.8 kBb09710187ad3b67be614a33a3cdd689f9355b507944e249dbb5c654348a75764
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 433.9 kB44aadb09d096e2573dabd4c1ece0e51faa5eb245d0315299c799d3dec3d2225c

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 NWM on mpw-005

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

Mask layer
NWM — N-Well*
Process step
17 — N-well mask
Drawn layer
64:20
Layer on the mask-layer GDS
21:0
Detail
NWM = nwell sized (cnwm.3a outside hvi / 3b inside), with DEPMOS/pwell-resistor exemptions
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.