mpw-002 / DNM Deep N-Well

the DNM shapes of all of mpw-002

40 of 40 dies rendered.

343,380 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 120.0 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-002_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-002_DNM_reticle.png
the stitched reticle, full resolution
3.7 MB 5b2de056d213c89e06747da956f4165268b4926947c7801f4eb28f525073492b2b85ccbe d147cf6189aacb7e3beee2bb5fb51f3ad75479455274c28ccbe477927dfdfc5b
mpw-002_DNM_reticle_overview.png
the same image scaled to fit 4096 px
187.5 kB b218ec8ccfc95b938ad9d96548db0c5a9357166b61c7b975d89cf3f783299690d89b0427 9db1866396d66e45457d6d6fb388a554582c4e9c83501373f34fac10c507fd77
mpw-002_DNM_tiles.json
where every tile belongs, and its checksum
13.2 kB 1968192e55ad0d8b0bae87f42226351be34b3293debb651812622ee75bf4ace06f099cc2 c6817a3c4028e9eb9557ed65879cd0bc0c766cbe730443913f1922556d397a3f
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, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A2 wishbone_can 7576×107767576, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A3 Analog-Neural-Network 7576×1077615152, 0 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
A4 caravel_dsp2 7576×1077622728, 0 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
A5 eFPGA_v3_caravel 7576×1077630304, 0 112.6 kBc44c9116bf1ff5b093ef69ceec04df0837845609c41dadfdbf571d1da80f6981
A6 opencryo_testchip 7576×1077637880, 0 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B2 lexicon 7576×107767576, 10776 112.6 kB35dbbb9ed8878f329344dc13b20a185dcf499a78a1c20f79ee9f99f81bcd3240
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 112.7 kB13db73c9160ab7481f8c2933959d0d67ebcaceb4ff7c0a939b5017dda65de899
B4 yonga-lz4_decoder 7576×1077622728, 10776 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
B5 Potentiometric_Digit 7576×1077630304, 10776 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
B6 fuserisc_ver2 7576×1077637880, 10776 112.5 kB54c31da14fee704f2812363bf6156d900933b953f94f9eae912fd49b5ab78251
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 115.5 kB1e9c11a3000ab8b4eacb71fa5d31259f5e45a1399b7bd783bd1d8a25d540443d
C1 Comparator_3v3 7576×107760, 21552 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
C2 caravel_multi_encode 7576×107767576, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C3 kasirgalabs_uart 7576×1077615152, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C4 Automatically_genera 7576×1077622728, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
C7 caravel_azadi_soc 7976×1077645456, 21552 116.5 kBb7e9d0dcd6f5ff3361092563f94ef0402f59f9583ce6d405491839888770011d
D1 caravel_dsc 7576×107760, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D3 caravel_user_project 7576×1077615152, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D4 General_Purpose_Band 7576×1077622728, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D5 zero_to_asic 7576×1077630304, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D6 fast_gcd_for_large_i 7576×1077637880, 32328 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
D7 Columbus 7976×1077645456, 32328 115.5 kB1e9c11a3000ab8b4eacb71fa5d31259f5e45a1399b7bd783bd1d8a25d540443d
E1 Space_shuttle 7576×107760, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E2 subservient 7576×107767576, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E3 caravel_analog_fulgor 7576×1077615152, 43104 111.5 kBb8c2c6835512fa8c424909fe4e42a423a9895ce6cd1117f822442aba4823db66
E4 bandgap_reference_de 7576×1077622728, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E5 renml 7576×1077630304, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E6 io_expander 7576×1077637880, 43104 112.4 kBa4cc347f80f8f04bf350c4facf5135bd7bb19ad3dcb15c4a5d7a84d7b53ffc30
E7 digital_pll 7976×1077645456, 43104 115.5 kB1e9c11a3000ab8b4eacb71fa5d31259f5e45a1399b7bd783bd1d8a25d540443d
F1 zero_to_asic 7576×111760, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F2 caravel_fpu 7576×111767576, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F3 BrqRV_EB1 7576×1117615152, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F4 hilas 7576×1117622728, 53880 115.3 kB29d710ed5497f738e43c7f6cb6070c2f401e6d61d2bfc399133b06c567152af2
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 115.3 kB29d710ed5497f738e43c7f6cb6070c2f401e6d61d2bfc399133b06c567152af2
F6 sha1_engine 7576×1117637880, 53880 116.2 kB52938880fa8e3afc0c918a7e19fe411bd630d345ea8bddcff92ba647775d00a2
F7 caravan_openfasoc 7976×1117645456, 53880 120.0 kB7cbbe23453760ae11c93f002c423a77a050433dac0d588ee982023b34b24e584

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-002

Resolved: reticle 5CS8007AC, run mpw-002, 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-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.