mpw-005 / CAPM Capacitor MiM

the CAPM shapes of all of mpw-005

40 of 40 dies rendered.

5,384 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 117.0 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-005_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-005_CAPM_reticle.png
the stitched reticle, full resolution
3.5 MB 0a0875e6bc593cb941b0c71be35096c1fd6a2081dc9474fb314e5a7686d5122556acf71f 25178af720f01ef24273db13f0e3adcd6c304e627bcb36b1385c2260c7b63419
mpw-005_CAPM_reticle_overview.png
the same image scaled to fit 4096 px
19.3 kB a2aa5f5b75d0a3da62134e7684366c02f5be97d3309fce1bbe1259f684f604517e63d055 6b6ad54edc605c0abfe0448a28c5b3d821de8558e3d73193767e932d6385b028
mpw-005_CAPM_tiles.json
where every tile belongs, and its checksum
13.5 kB 766dfffe029fcb79898e799f96ecffbe62d9684a83aa4557922f3c3f69849dafefefd4ed 3eb09962e6c57fd51a7bd6b7a446973c5981155c50ca4797cfc6eac333e96421
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 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 103.1 kB51a788bc4ab8fb1c86a77e2c69be064a68f946aa1fea431d7b63107e0da92636
A2 mpw5_cache 7576×107767576, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B2 microwatt_mpw5 7576×107767576, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B3 armleo_gpio_mpw5 7576×1077615152, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B4 fabulous_efpga 7576×1077622728, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B5 i2c_controller 7576×1077630304, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B6 lbist-mbist 7576×1077637880, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
C1 riscduino-qcore__q0_ 7576×107760, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C2 high_speed_vco 7576×107767576, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C3 openfasoc-cryo-gen 7576×1077615152, 21552 117.0 kB9d51a30f621f8029198dd5aef6bec13deaf4124fa65cdb335e8737f03a911a5c
C4 microwave_signal_generator 7576×1077622728, 21552 105.0 kB16661487325fe1ca550a949ccbe07353d6eb78340af5e5e6a3e9eb41f890685e
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 103.7 kB43381721e7e42505c29643a1964e7d7a4d87c861b0147bea529754f4212667bb
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D2 azadi_iii 7576×107767576, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D3 tia_for_physic_experiments 7576×1077615152, 32328 103.2 kB6aa41b1fb71d035e59d00c9c9936fcbb3e1db3d4337b8818d2f4a8eb0cdd6be0
D4 qf105 7576×1077622728, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D6 junga_soc_mpw5 7576×1077637880, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D7 nand_flash_mpw-5 7976×1077645456, 32328 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
E1 kasirga-c0-mpw-5 7576×107760, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E3 wirelessenergyharvesting 7576×1077615152, 43104 106.1 kB73f10c95bf64203c6996c6ea87884e1030f10e04ea387bcb1ab4f73903122f69
E4 caravel_raster_engine 7576×1077622728, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E7 alu 7976×1077645456, 43104 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
F1 4ft4 7576×111760, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F2 rng_chaos 7576×111767576, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F3 uetrv-ecore 7576×1117615152, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F4 systolic_array 7576×1117622728, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F5 reram_test 7576×1117630304, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F6 secure_memory 7576×1117637880, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F7 asynchronous_fibonacci_counter_testfix 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-005

Resolved: reticle 5CS8011AC, run mpw-005, mask CAPM

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

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/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.