mpw-002 / CAP2M Capacitor mask

the CAP2M shapes of all of mpw-002

40 of 40 dies rendered.

176 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 (4.4 MB in total, largest 110.9 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-002_CAP2M_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_CAP2M_reticle.png
the stitched reticle, full resolution
3.5 MB 08b61830ecd07b27ef7bbd9c6edeb5ca2976c118ecf71e9d7bcaa0d5e45419686a6c33c2 10e66d595438778de3d8a9b1eb03765a5559bab71a70fc5eeded51f9b915ea63
mpw-002_CAP2M_reticle_overview.png
the same image scaled to fit 4096 px
15.8 kB 83daa9101b1bb94aef280508af3f456588759efeeff9b0bb3a5644f3f9959763859f485e 96dd295eca4d9b0a44c899e90046dcef80d03efa864154116175bf4e220dab2a
mpw-002_CAP2M_tiles.json
where every tile belongs, and its checksum
13.3 kB 7efa2ca774df01ea7b1edb2cb179dd864e875810f6877be216286f10a705d3c298cf91f8 a44c6b3aab73050df0a8c81852431a9b2a32f72a14566348c79e7ae931556f9e
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, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A2 wishbone_can 7576×107767576, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A3 Analog-Neural-Network 7576×1077615152, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A4 caravel_dsp2 7576×1077622728, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A5 eFPGA_v3_caravel 7576×1077630304, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A6 opencryo_testchip 7576×1077637880, 0 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B2 lexicon 7576×107767576, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B4 yonga-lz4_decoder 7576×1077622728, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B5 Potentiometric_Digit 7576×1077630304, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B6 fuserisc_ver2 7576×1077637880, 10776 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
C1 Comparator_3v3 7576×107760, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C2 caravel_multi_encode 7576×107767576, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C3 kasirgalabs_uart 7576×1077615152, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C4 Automatically_genera 7576×1077622728, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
C7 caravel_azadi_soc 7976×1077645456, 21552 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
D1 caravel_dsc 7576×107760, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D3 caravel_user_project 7576×1077615152, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D4 General_Purpose_Band 7576×1077622728, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D5 zero_to_asic 7576×1077630304, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D6 fast_gcd_for_large_i 7576×1077637880, 32328 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
D7 Columbus 7976×1077645456, 32328 106.9 kB60ca2196d8ee88e8743dee718a892b494aa1384fc91ecb15b30d24679dbbd62d
E1 Space_shuttle 7576×107760, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E2 subservient 7576×107767576, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E3 caravel_analog_fulgor 7576×1077615152, 43104 105.1 kB18ca76da4b33795a155959e591c5b0f385d077cd62490e543c4d62e6e671a1bd
E4 bandgap_reference_de 7576×1077622728, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E5 renml 7576×1077630304, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E6 io_expander 7576×1077637880, 43104 102.9 kB9b0aeae35c7144155b07a1c5523bf24a8df5090971ab36c4d234d3ced6633c8b
E7 digital_pll 7976×1077645456, 43104 107.5 kB1c2c229f5b854306d87c93449d8b23717b1048a47af780d5a6a5afd957ad2560
F1 zero_to_asic 7576×111760, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F2 caravel_fpu 7576×111767576, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F3 BrqRV_EB1 7576×1117615152, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F4 hilas 7576×1117622728, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F6 sha1_engine 7576×1117637880, 53880 106.7 kBbe2094b091fea5063fa1ca18b9345c3fcc68f1906a346b29248e6851741c50f6
F7 caravan_openfasoc 7976×1117645456, 53880 110.9 kBea0be884384754165c29bf0468e1ee1a1f67ad1c71ab09fb6c962f232b604ebe

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 CAP2M on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask CAP2M

Mask layer
CAP2M — Capacitor mask
Process step
152 — Capacitor mask
Drawn layer
97:44

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.