mpw-008 / LI1M Local Intrcnct 1

the LI1M shapes of all of mpw-008

40 of 40 dies rendered.

269,025,763 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 (300.1 MB in total, largest 18.5 MB), 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-008_LI1M_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-008_LI1M_reticle.png
the stitched reticle, full resolution
228.4 MB 40d334062b2082b7145ce77c7235e81d9a9e03042e6134d51a796a3a494e5fb5671280ce b3d64f7a317d64d8d3da837aaa926430d3545df33d190df4879e294b1305a59f
mpw-008_LI1M_reticle_overview.png
the same image scaled to fit 4096 px
5.2 MB 9ea6fe6d7ae39efdf957c9acf8f53b9f387f015e9d1faec331ce0fadad9dcee3f7ae8d49 046ecbc4df1563b34027b0f8f38b6c4815c60f08b6cb5d672313299097ec7c18
mpw-008_LI1M_tiles.json
where every tile belongs, and its checksum
13.5 kB 1b132141002df2651363a87ac9fb9e3d6c38c8ee427c55ee2d115e8d20f1faf939688023 779cbd19fcfde5334b70bc42d99da62e4914064ca3d93b75cf52f2090e7cd4cb
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 wishbone_pio 7576×107760, 0 7.4 MBf84852a80616a4ee0d0c45b4beaf4ce9bbf9054672bdfec9eaa8e36f894cc044
A2 iit_indore_mm 7576×107767576, 0 5.4 MB3fdb6ffab12a22b2352c3357ad131043ba53990eafc7bcb8d200db21058211a6
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 5.4 MBab0f2e0ad540585a95ace18800c8fef357dd923a2ed17e203d50145de9facb69
A4 riscduino-dcore_d4_ 7576×1077622728, 0 13.6 MB038342b1595ae3fbb50057c27d17a3568979155cfc20524ea576e9abcac1c31d
A5 riscduino-qcore_q3_ 7576×1077630304, 0 14.2 MB186e665d5aa098b77c9a6b56b998bb5b45042614f276fa7477fb0d6b449d0df9
A6 caravel_mpw8 7576×1077637880, 0 5.6 MBc82bbe163633932f51b5d2b5a76ca54939bcca39e3de1ac86d0757e819fd3a73
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 5.9 MBf88c8335e0cbe92eacfcbbcf20dffbccce9dd251912f242941bb265b70c40442
B2 sramtestvehicleg4 7576×107767576, 10776 5.5 MBd53adc7f0dc4550712286b629ab4e9ff01db830d1918c7d48b7e5b81128004c3
B3 reram_crossbar_rerun 7576×1077615152, 10776 6.0 MB47488d42298a81c8e2c7146aae69df4be1e8789273e7f7dedf94b21884bc0a16
B4 tinytapeout_skullart 7576×1077622728, 10776 9.4 MB45684df76ff2f26b556afd90c5b13076e7c067ba9fa1026263237a9fa0908d16
B5 soomrv__mpw-8_ 7576×1077630304, 10776 18.5 MBf293e878349c4d8c53dcd2da40a0d641f892df2f6d4859a78b9c0753a261733d
B6 clock_divide_selector 7576×1077637880, 10776 5.2 MB795e99f4b9416ffff01a44729a61522c9714ea5321352c108fe089c7e9ec6343
B7 reram_module_mpw8 7976×1077645456, 10776 5.2 MB1789f231a79c7f7193b246b072ee6a0cae8511c1fd409845ab8003692dce441c
C1 shapingthewaves_8 7576×107760, 21552 10.4 MB4a7194e6b2cad439ad1f7c0c6ebbb3b6f0a969fbf9f9a00c0a4c1a40f98e3f22
C2 leosoc 7576×107767576, 21552 8.8 MB0f040e97a8c8ce9ac143468168d2d03bc520ad2889163aa5c1c20c2c2b1cc651
C3 decrypt_aes128 7576×1077615152, 21552 8.5 MB41c26ba1ecc46818ff5a7c6f05e611da4f54f34ce42a101b73a28e1917dd1687
C4 approximate_adder_axha 7576×1077622728, 21552 5.4 MBd61b82f67f35eebd9f084024a0e842fdf1c0ce8248e00b8444d7c61fa87c21e5
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 8.6 MBc14362116d3a8e5679f838b098a94139f5892af205f013b02730c6e3f51eee4e
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 5.2 MB9dfe2869565e0b03237007343c1487926fd94849f5f3a3f3d4d195805e20b9d7
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 17.8 MB43ef545cd64c5457d98953be1f4e658165773741050380b6ab89e5abd9503b51
D2 hyperram_controller_for_sky130 7576×107767576, 32328 5.4 MB95f049e9f6e14d5aeb7599fd1fb93ecf8f68266f2a4d14c4c13859b2d421b801
D3 hardened_alu 7576×1077615152, 32328 5.7 MB0a7e3fffce585e8370fa1e6a05c49a3b07b7994188426e33c915183931809908
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 10.0 MB3554732a5cff94feafe4b864eba5c29f037470b023ae90dbb01fdbdc25e98d78
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 5.1 MB51683f0774080bf48f99d809a55f7a5b94edd417d174387fb4609aa379dbd5f1
D6 gf180_vs_sky130 7576×1077637880, 32328 7.8 MB70f0671544e2e7bd0c92834fdbe08c817c51711e2a8b3dea37440955c4e41fbc
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 5.4 MBc2b7891ebc0d400a7f70b343cc45240143b37c91c84c6fa7b43356a24d588413
E1 iiitb_sd_mpw8 7576×107760, 43104 5.4 MB3d8150576dee34e3902701d653ccc208b3e5c1b063c67f18e1f626151a9cd8c2
E2 iiitb_sfifo 7576×107767576, 43104 5.5 MB3cec2cdf1170898b0624a15b4c681eed630c7d9ab6bb3cfb705afd01a0b260fc
E3 iiitb_alu 7576×1077615152, 43104 5.5 MBf33dc155be4fde6672d5048ba75c01baa469edecc58302c593cbd26b36146455
E4 ppcpu2 7576×1077622728, 43104 13.1 MBfec10b3fa8a181f8111ee5fc68fc1475c84b880cda0ce8e3cc6fd5e6060b36c9
E5 mega4_reram 7576×1077630304, 43104 6.2 MB20ac05ca3376dfce0c1ea02613f7cef3c894bf5f44b8a0b9f885425deec66494
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 5.3 MB83a5b6d73582a973a55e436bd4bcf3b34f2db97f90ca0a3b27ba99139c347398
E7 sky130cla 7976×1077645456, 43104 5.3 MB835894d8328dfdad6f72da9cd58564c4ecf4307c61c58ae707aa31069222f8dc
F1 uetrv_esoc_v2 7576×111760, 53880 7.4 MB85c9c8c32207b62f24f5bb4fe123434461ec2d8f4f972bfea6c831ca21203b57
F2 sequencedetection1010 7576×111767576, 53880 5.4 MBcea007e3fde9cdf00c14b684d5df4efc66734e838a5555548f9db3e4b74627f3
F3 testasynctrimux-mpw8 7576×1117615152, 53880 5.3 MB1827a081aac3c9abc2d57d13744510a84bacf7048eec4c9ba8301015824f5c5a
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 5.3 MB55f6b867d6252a3637ecbdf9dbb130fc7e01ac4b62c7ac7e12995c1c38d7c0bf
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 5.4 MB30680061a0706a49ca8d43264c92d909bc91abaad727ccc8d83fd48f626d4283
F6 arbiter_puf 7576×1117637880, 53880 5.3 MB856415b7246c4a689d4675f3f7fef08e81c57b61fd52ec66517887a812c753df
F7 unigate-sky 7976×1117645456, 53880 7.8 MBc9c2adca8981152f39ce649cde91d9d6b342a6aa2af99bfa94e6199aad87150e

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 LI1M on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask LI1M

Mask layer
LI1M — Local Intrcnct 1
Process step
102 — Local interconnect 1 mask
Drawn layer
67:20
Layer on the mask-layer GDS
56:0
Fill layer
56:28
Also
LI (Metal 0)
Used in SKY130
yes

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/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.