mpw-004 / DNM Deep N-Well

the DNM shapes of all of mpw-004

40 of 40 dies rendered.

19,980 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 119.5 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-004_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-004_DNM_reticle.png
the stitched reticle, full resolution
3.7 MB 91d42e9c4c6f2b165e1492d97c4827bde2662c3eee4f6b2a2e893e8c4fd330dea8486cec 3434aaf6811ebd44215d47f05d5e34caec388a4a99a6dfdb29f1f5735e1139fa
mpw-004_DNM_reticle_overview.png
the same image scaled to fit 4096 px
188.0 kB 0090b1676f7c8c9729b5e12d3223627e5eb093c0081fd7d506255112d33435c36d46e324 1ead01a9db827c9bd91cc67ddad460e1daa65112d9d05ec6914789861ea5d178
mpw-004_DNM_tiles.json
where every tile belongs, and its checksum
13.3 kB 4a7db89653cb7e7060ff9c79e965b8f17fa08b5d006211110beea906fcd9714fe52a882d 0cea4cbdc1714e49d73752b57a77f7d7c7f77b09ddea43c0ce4d6ab456f419c3
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 zero_to_asic_course_group_submission_mpw4 7576×107760, 0 112.4 kBfdbbb041d734ec1c7b9c3714a47ed32ad1519a777a60a5d79dc8e982ef1bc08d
A2 zero_to_asic_course_mpw2_re-run 7576×107767576, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A3 cmos_only_self-biased_current_source 7576×1077615152, 0 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
A4 pifive-soc 7576×1077622728, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A5 ram_generator 7576×1077630304, 0 112.5 kBc94f5c8cdda1439bf027e10a99ba8cc59134425eeee4850dd4d3174caf5b151e
A6 space_controller 7576×1077637880, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 randsack_b1 7576×107760, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B2 4t1r_testchip 7576×107767576, 10776 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
B3 yonga-turbo_encoder 7576×1077615152, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B4 icesoc 7576×1077622728, 10776 112.5 kB54c31da14fee704f2812363bf6156d900933b953f94f9eae912fd49b5ab78251
B5 key_value_store 7576×1077630304, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B6 subservient-mpw4 7576×1077637880, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B7 logic_bist 7976×1077645456, 10776 116.7 kB8f130ac67a3734badc974ed63b03b75cd88797c0fd9fcf1362911631a9e42551
C1 riscduino-r1 7576×107760, 21552 112.6 kBf0ca1246ab43789f60ad4b27f403f7c6e6c8020e1f13f654f42ec5f80c63f24c
C2 template 7576×107767576, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C3 vexriscv_soc 7576×1077615152, 21552 112.5 kB5709f7473c96b7b5b2d6be134855d89ba2e196e495ab968dd4d6460c07614572
C4 ring_oscillator 7576×1077622728, 21552 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 an_mssro-based_vcro 7576×1077637880, 21552 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
C7 approximate_multiplier 7976×1077645456, 21552 116.5 kBb7e9d0dcd6f5ff3361092563f94ef0402f59f9583ce6d405491839888770011d
D1 icd_fast_nu_mpw4 7576×107760, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D2 rram_testchip 7576×107767576, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D3 rest 7576×1077615152, 32328 112.7 kB24e3dfa91efa20eabce4fb075579b9fefc86be7ae4e4406fb26903a9fb02c839
D4 fault_tolerant_16-bit_alu_design 7576×1077622728, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D5 sky130_radtol_test_chip 7576×1077630304, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D6 two_stage_cmos_opamp 7576×1077637880, 32328 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
D7 ks-guitar 7976×1077645456, 32328 116.5 kBb7e9d0dcd6f5ff3361092563f94ef0402f59f9583ce6d405491839888770011d
E1 ks-guitar-2s 7576×107760, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E2 yifive_a2 7576×107767576, 43104 112.6 kBf0ca1246ab43789f60ad4b27f403f7c6e6c8020e1f13f654f42ec5f80c63f24c
E3 coriolis_test_soc_-_mpw4 7576×1077615152, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E4 sar-adc_and_analog_circuits 7576×1077622728, 43104 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
E5 junga_soc_mpw4 7576×1077630304, 43104 112.5 kB5709f7473c96b7b5b2d6be134855d89ba2e196e495ab968dd4d6460c07614572
E6 ibtida-ii 7576×1077637880, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E7 azadi_ii 7976×1077645456, 43104 116.7 kB8ea2a00cee3162198a38be6079237103281338c4971831344ecaae8915e9f9de
F1 open_source_configurable_fpga_fabric 7576×111760, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F2 current_starved_vco 7576×111767576, 53880 115.3 kB29d710ed5497f738e43c7f6cb6070c2f401e6d61d2bfc399133b06c567152af2
F3 updown_counter__test_ 7576×1117615152, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F4 skullfet 7576×1117622728, 53880 115.3 kB29d710ed5497f738e43c7f6cb6070c2f401e6d61d2bfc399133b06c567152af2
F5 cmos_rail-to-rail_comparator 7576×1117630304, 53880 115.3 kB29d710ed5497f738e43c7f6cb6070c2f401e6d61d2bfc399133b06c567152af2
F6 kasirga_k0 7576×1117637880, 53880 116.3 kBb7809cf3239425102f18560adc5cf40e1fb2d0d65ae2265231fa3f457e7943d1
F7 iotestvehicle 7976×1117645456, 53880 119.5 kBb5c7e49ca3610b592b1ea85c14f4717b238ed55432407d0bd3ad26be7858b3c3

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-004

Resolved: reticle 5CS8010AC, run mpw-004, 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-004 — SKY130 MPW-4 (original)

Foundry product
EF MPW#4 (+ 'With ReRAM' split lot)
Reticle set
5CS8010AC
Run date
2021-12-31
Submissions closed
2021-12-31
Projects
52
Fab lots
4216266
4216266
4216266
Identification
confirmed — Lot records: EF MPW#4, lot 4216266 = 'SKY130 MPW 4'; the set is absent from the 2026-08-25 return shipment (destroyed 2025-06) — see 5CS8018AC
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-4 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-004/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.