mpw-003 / DNM Deep N-Well

the DNM shapes of all of mpw-003

40 of 40 dies rendered.

343,616 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 120.5 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-003_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-003_DNM_reticle.png
the stitched reticle, full resolution
3.7 MB a4b3657d0c6337b780f875a3063049462b1c1a79149bda7f8ac46ee05991ac0fd5be7724 92d4d8b03a77cbf833e7491a7040de6f6a070f2c0fbe56ea3bba14408e31e585
mpw-003_DNM_reticle_overview.png
the same image scaled to fit 4096 px
186.3 kB b1ae7d49021f670e5b01f641e46ce1559283c74c63c30770a8779d3b23912e95681eaa9c e2a371c8c1249caa3a320ccc7dfa91ce1db8d65915c2ad80dfeb1f91a7f15151
mpw-003_DNM_tiles.json
where every tile belongs, and its checksum
13.4 kB 0f84d52018d212cbcc34676e7ba0820ecf1cbdbd5a34c88e0f478f95b300a3feba9a802e ae222da3bfcf33d1cc649af28c7d1191f60dabd5bfbbf5e71c7ae0b3e459725c
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 stdcelllib 7576×107760, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A2 1v8_ldo_design_in_skywaters_130nm 7576×107767576, 0 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
A3 openram_test 7576×1077615152, 0 112.4 kBfe942d19315afd09a3e7fc82fa7fc72dd87a38b2bccc54fc09ce11d9da47835b
A4 zero_to_asic_course_group_submission_mpw3 7576×1077622728, 0 112.4 kBe3be9e4fe5d93034fbdada2a04b67257f9f1af41444787aed03af0d1cc4885a1
A5 crypto_accelerator_v2 7576×1077630304, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A6 picorf0 7576×1077637880, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 randsack 7576×107760, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B2 yonga-100m_ethernet 7576×107767576, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B3 approximate_multiplier 7576×1077615152, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B4 vsd 7576×1077622728, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B5 asic_key_value_store 7576×1077630304, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B6 mbist_controller 7576×1077637880, 10776 112.6 kB98a29153e7de40677e0e898d2f3fb12f8af299d07e462f05b98b0fd685e1d7a0
B7 riscduino 7976×1077645456, 10776 116.6 kB326b6ca4afa2063cb710daaae4d03f73c7c260a24d7e7fec711e685f474798a6
C1 caravel_peripheral_extender 7576×107760, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C2 ucsc_openram_test_chip_v2 7576×107767576, 21552 112.7 kB13db73c9160ab7481f8c2933959d0d67ebcaceb4ff7c0a939b5017dda65de899
C3 class-d_audio_amplifier 7576×1077615152, 21552 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
C4 efabless_processor 7576×1077622728, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 tooltest 7576×1077637880, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C7 comparator_and_mixed_signal_test 7976×1077645456, 21552 115.5 kB1e9c11a3000ab8b4eacb71fa5d31259f5e45a1399b7bd783bd1d8a25d540443d
D1 current_starved_voltage_controlled_oscillator 7576×107760, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D2 two_stage_cmos_operational_amplifier 7576×107767576, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D3 sziklai_pair_amplifier 7576×1077615152, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D4 two_stage_cmos_opamp_with_frequency_compensation 7576×1077622728, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D5 pre-trained_neural_network_for_mnist 7576×1077630304, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D6 rotfpga 7576×1077637880, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D7 treepram 7976×1077645456, 32328 116.5 kBb7e9d0dcd6f5ff3361092563f94ef0402f59f9583ce6d405491839888770011d
E1 treepram-red 7576×107760, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E2 caravel_jacaranda-8 7576×107767576, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E3 sudoku_accelerator 7576×1077615152, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E4 fixed2float_converter 7576×1077622728, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E5 high_speed_adder 7576×1077630304, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E6 my_proj 7576×1077637880, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E7 pwm_test 7976×1077645456, 43104 116.5 kBb7e9d0dcd6f5ff3361092563f94ef0402f59f9583ce6d405491839888770011d
F1 elpis-light-mpw3 7576×111760, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F2 vsdbabysoc 7576×111767576, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F3 vsdmemsoc 7576×1117615152, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F4 caravel_sram_test 7576×1117622728, 53880 116.3 kB002e9b41320a3c48be22ea77df5c333cd6f49798ba252df791c199c37e4e5689
F5 8-bit_sar-adc_with_offset_calibration 7576×1117630304, 53880 115.3 kB29d710ed5497f738e43c7f6cb6070c2f401e6d61d2bfc399133b06c567152af2
F6 caravel_hyperram_driver_and_openram 7576×1117637880, 53880 116.3 kB94404a26b4e34e2b6d0b901a4bb146c06fa82dc8331fdbf97f70b2ba0b10d9bc
F7 caravel 7976×1117645456, 53880 120.5 kB84280fc0fc5756228064793972320a2bbd7b01033023e27e55062edaf23b5951

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-003

Resolved: reticle 5CS8008AC, run mpw-003, 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-003 — SKY130 MPW-3

Foundry product
EF MPW#3
Reticle set
5CS8008AC
Run date
2021-11-15
Submissions closed
2021-11-15
Projects
53
Fab lot
4206521
Identification
confirmed — Lot records: EF MPW#3, lot 4206521 = 'SKY130 MPW 3'; confirmed by a photo of the MPW-3 mask set's VIM2 plate label (DEVICE 5CS8008A-A, LAYER VIM2 560A 03-01, archived 2022-05-13), seen 2026-08-28
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-3 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-003/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.