mpw-008 / CAPM Capacitor MiM

the CAPM shapes of all of mpw-008

40 of 40 dies rendered.

123,722 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 (4.5 MB in total, largest 204.5 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-008_CAPM_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_CAPM_reticle.png
the stitched reticle, full resolution
3.6 MB 1987c63806d2eb067cdaac38d107aab2f9bb9facbd484e1846f7d5bbef1dc80eec63238f d559e09c327636d102e2e47e59db16c25cc96bd2c9098a678e688d330cbeea9f
mpw-008_CAPM_reticle_overview.png
the same image scaled to fit 4096 px
64.3 kB c6a473af58243b6b5e93a4d635c405c78c8dab977c89bb661af7ad43a6db4ee93976b7c6 52bd60e7396c31391cc32dad99761d142e242afd604106fe7254f8a87f5ed40f
mpw-008_CAPM_tiles.json
where every tile belongs, and its checksum
13.5 kB 63601cc51e01a39d29d77c9443512f57b4f1b4e5240c8f6cce2597ec6655a586a59f71e4 555419971399a7e9ffa4ad4759d26d4c848b0ee9a5e29b339fba16b2fc3f5851
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, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A2 iit_indore_mm 7576×107767576, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A4 riscduino-dcore_d4_ 7576×1077622728, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A5 riscduino-qcore_q3_ 7576×1077630304, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A6 caravel_mpw8 7576×1077637880, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 115.6 kBb559dbcb0d3495346540ea0d728262fc71d920af5fd71bd6536bd76a2b3cc540
B2 sramtestvehicleg4 7576×107767576, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B3 reram_crossbar_rerun 7576×1077615152, 10776 204.5 kB9874f76a13505b19ef751cb60807d35747ff1e31df8429465127c041dae71ade
B4 tinytapeout_skullart 7576×1077622728, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B5 soomrv__mpw-8_ 7576×1077630304, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B6 clock_divide_selector 7576×1077637880, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B7 reram_module_mpw8 7976×1077645456, 10776 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
C1 shapingthewaves_8 7576×107760, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C2 leosoc 7576×107767576, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C3 decrypt_aes128 7576×1077615152, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C4 approximate_adder_axha 7576×1077622728, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 107.3 kB6fe830d8e1a0cf2d0a3780d8a506c2bb465126870db57649cb3b64374c802a0a
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D2 hyperram_controller_for_sky130 7576×107767576, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D3 hardened_alu 7576×1077615152, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 103.1 kBc34710ef3fb889e61a1a6c4ffb9ef217fca99f4b0e5d21de76a8e9113cea851b
D6 gf180_vs_sky130 7576×1077637880, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
E1 iiitb_sd_mpw8 7576×107760, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E2 iiitb_sfifo 7576×107767576, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E3 iiitb_alu 7576×1077615152, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E4 ppcpu2 7576×1077622728, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E5 mega4_reram 7576×1077630304, 43104 106.4 kB83d2554bbf8e03de45e8bcf39a95587b2b35f8c37a95af9a3aa489ca357890ee
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E7 sky130cla 7976×1077645456, 43104 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
F1 uetrv_esoc_v2 7576×111760, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F2 sequencedetection1010 7576×111767576, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F3 testasynctrimux-mpw8 7576×1117615152, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F6 arbiter_puf 7576×1117637880, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F7 unigate-sky 7976×1117645456, 53880 110.9 kB5fbd4417aa5c04604eca90097c7419b94a4f810eb592fea229db184fd64d801c

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 CAPM on mpw-008

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

Mask layer
CAPM — Capacitor MiM
Process step
137 — Capacitor mask
Drawn layer
89:44
Used in SKY130
no

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.