mpw-002 / CAPM Capacitor MiM

the CAPM shapes of all of mpw-002

40 of 40 dies rendered.

395 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 112.0 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-002_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-002_CAPM_reticle.png
the stitched reticle, full resolution
3.5 MB 44445c38df16ffc7c8b574da261db8f3cca85771b9885d8de3c38e5f6af439812ad8c893 d2112d59557be5792379ddb53894f233d35cbfdacfad6ef361286b70159e74df
mpw-002_CAPM_reticle_overview.png
the same image scaled to fit 4096 px
16.9 kB 6f898c25316fd501ed9601d99cffeae88b87ccb1c6b0e704be2097e8c485c64fe8438e99 39d53f0faa282528b53b531a52448d121821b5fd4a49851f232b49f1a3ddd2d0
mpw-002_CAPM_tiles.json
where every tile belongs, and its checksum
13.3 kB ccc82e7cc4629f67eb1f91efe6da3b9c3f9ddf306b8274fb571cf1dd08d37a4e8cfc22a1 85a230a0e6c3ef8a5a400ca6bffed385d0ede4ca71fdd4a8965ec8f5153f553b
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 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A2 wishbone_can 7576×107767576, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A3 Analog-Neural-Network 7576×1077615152, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A4 caravel_dsp2 7576×1077622728, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A5 eFPGA_v3_caravel 7576×1077630304, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A6 opencryo_testchip 7576×1077637880, 0 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B2 lexicon 7576×107767576, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B4 yonga-lz4_decoder 7576×1077622728, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B5 Potentiometric_Digit 7576×1077630304, 10776 103.0 kBa666035515da33865875a896470588e696dfdffad3199db8cdd8a6b5600377d1
B6 fuserisc_ver2 7576×1077637880, 10776 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 107.4 kBb9e8a85635d1c729938c1944eb5a1533f08bdc6bf4931c7625ba1896c2fa629e
C1 Comparator_3v3 7576×107760, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C2 caravel_multi_encode 7576×107767576, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C3 kasirgalabs_uart 7576×1077615152, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C4 Automatically_genera 7576×1077622728, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
C7 caravel_azadi_soc 7976×1077645456, 21552 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
D1 caravel_dsc 7576×107760, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D3 caravel_user_project 7576×1077615152, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D4 General_Purpose_Band 7576×1077622728, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D5 zero_to_asic 7576×1077630304, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D6 fast_gcd_for_large_i 7576×1077637880, 32328 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
D7 Columbus 7976×1077645456, 32328 107.5 kBf396d24ebed32276e2f43a8fa601743141c93015709aa0ec3732333fe6641cd5
E1 Space_shuttle 7576×107760, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E2 subservient 7576×107767576, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E3 caravel_analog_fulgor 7576×1077615152, 43104 104.7 kB45c1ad01528ca41b5cb1d4b0a7feb9e06c506baee1a025a4411c702456ca8648
E4 bandgap_reference_de 7576×1077622728, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E5 renml 7576×1077630304, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E6 io_expander 7576×1077637880, 43104 102.9 kBa0dbd42a8e3b2535aea82ed594c78cf0746bdbfac6522550c6e0f58882dce2af
E7 digital_pll 7976×1077645456, 43104 106.9 kB293f7b3bb80ecc8189b598f4d0936a4ee71b39727ca6a3f6013d6d774509850c
F1 zero_to_asic 7576×111760, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F2 caravel_fpu 7576×111767576, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F3 BrqRV_EB1 7576×1117615152, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F4 hilas 7576×1117622728, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F6 sha1_engine 7576×1117637880, 53880 106.7 kBb2d0c106177c99cb6a58b0f92661ce84240bd93274f4d79f662ff5a6a8b7593b
F7 caravan_openfasoc 7976×1117645456, 53880 112.0 kBd2593703ef2c9c7473e0a0abd4b8eae90fa1cd583795377050a7c0b10764e138

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-002

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

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

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.