mpw-007 / CAPM Capacitor MiM

the CAPM shapes of all of mpw-007

40 of 40 dies rendered.

120,116 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 208.4 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-007_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-007_CAPM_reticle.png
the stitched reticle, full resolution
3.6 MB 9c1f1929245270f4bde2434011a7853a208bbc13f2dafe90cd42ece38a6d6f89ea3b6e5c 17760580de69c998a15809ffb0a75cc6aefd332429bf0cda1bf7e3003ed8bb79
mpw-007_CAPM_reticle_overview.png
the same image scaled to fit 4096 px
52.2 kB f887107d5aeb7ba870c3ea2f665b3ccf3d676ddc4d60c6ada97d4ea9adffeb322de62c0a ba013e811169c5f09013be892f6755466812e971961f55c379eb24cfba2a104b
mpw-007_CAPM_tiles.json
where every tile belongs, and its checksum
13.5 kB 35595838d54b218b290d7cd53ac02adf95a4478c802137fafbc07f0af249f2f9503d3102 00b15b60df53a7ea7ccbe06dc5524d08696c0e29762afa0af039f53fc1843dcf
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 tinytapeout 7576×107760, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A3 microwatt_mpw7 7576×1077615152, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A4 yonga-can_controller 7576×1077622728, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A5 rocketalpha 7576×1077630304, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A6 riscduino-dcore_d3_3 7576×1077637880, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B2 riscduino-score-s4-3 7576×107767576, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 105.8 kBe1a7e4fe39c839d55e5abb9befbe01b15104d492e96f7fa2eb4205b875d49de4
B4 sramtestvehicleg3 7576×1077622728, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 103.1 kBdea727e97027c904a080b91a96ef42d0c8dc85be8f21a478c87d1d4e433f8b9e
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B7 reram_crossbar 7976×1077645456, 10776 208.4 kBeda433d3ba0e9223bc3854068d7c48b769518070f81196f09474215ee01e7f90
C1 trainable-nn 7576×107760, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 104.2 kBcd2623d692111bec344518a5ff5f5922a9a924b8135b8c55fbd0e63923bcc137
C3 bitcoin_mining_asics 7576×1077615152, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C4 yonga-mcu 7576×1077622728, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
D1 mixed_signal_circuits-jun13 7576×107760, 32328 103.1 kBc34710ef3fb889e61a1a6c4ffb9ef217fca99f4b0e5d21de76a8e9113cea851b
D2 fpga_programming_management_unit 7576×107767576, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D3 warp-v 7576×1077615152, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D4 leaf__mpw7_ 7576×1077622728, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D5 in_memory_computing_sram 7576×1077630304, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E2 in_memory_computing_rram 7576×107767576, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E3 rram_imc_v2_flat 7576×1077615152, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E4 topmetalse-dps 7576×1077622728, 43104 103.6 kB3feba43dbad0ec1f82a92b2feed1075db641a93a807fe275267264845df74f2e
E5 chaos_automaton 7576×1077630304, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E6 hehecore_resubmission 7576×1077637880, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E7 waveform_generator 7976×1077645456, 43104 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
F1 rift2core 7576×111760, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F2 rift2go_2300 7576×111767576, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F3 sloci_resubmission 7576×1117615152, 53880 107.9 kB6e43ba776b3227d33b78faee57dd84e764eb949f8a29b46cac16b6d74d82b58a
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F5 crypto_aes128 7576×1117630304, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F6 graphics_controller 7576×1117637880, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F7 prga-test 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-007

Resolved: reticle 5CS8016AC, run mpw-007, mask CAPM

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

mpw-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/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.