mpw-002 / HVNTM High Volt. N-tip

the HVNTM shapes of all of mpw-002

40 of 40 dies rendered.

554,466 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 (9.6 MB in total, largest 280.2 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_HVNTM_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_HVNTM_reticle.png
the stitched reticle, full resolution
5.3 MB d77280280087b364f47fc450a271d4e6c063761ae6e507c5d648087716c5adf6eea314e4 c4daae8ccb39ca424d4cee4e41122f353f9be105f49061f430761d96ed14964e
mpw-002_HVNTM_reticle_overview.png
the same image scaled to fit 4096 px
367.7 kB 9e4314e6146952251b3733f44016b8eba3a5738d655b2b4d1289a20b9c244650410580fd 4b12146a38f87d70163493763bfc22bd6cf58d6c80f5fa8f9945d23a6933c8ff
mpw-002_HVNTM_tiles.json
where every tile belongs, and its checksum
13.3 kB fc6ee2a3713bddb215fc6e62205c6875efef1280a842654b7ce2fffdef34ba16799f8ce1 9ed209e6b477654a10cba4888395481ffe29387d8e62a4d2d7e4e9e189487a87
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, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A2 wishbone_can 7576×107767576, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A3 Analog-Neural-Network 7576×1077615152, 0 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
A4 caravel_dsp2 7576×1077622728, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A5 eFPGA_v3_caravel 7576×1077630304, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A6 opencryo_testchip 7576×1077637880, 0 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B2 lexicon 7576×107767576, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B4 yonga-lz4_decoder 7576×1077622728, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B5 Potentiometric_Digit 7576×1077630304, 10776 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
B6 fuserisc_ver2 7576×1077637880, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 225.0 kBde4692c87ed66c18ae56f191f6de4556ea247abe383729a6c7e99aae20b8f0ae
C1 Comparator_3v3 7576×107760, 21552 220.6 kB37e28d6c74e8d1dedb92dc006935e07084115c40446b8f0f9d76e81a79bee095
C2 caravel_multi_encode 7576×107767576, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C3 kasirgalabs_uart 7576×1077615152, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C4 Automatically_genera 7576×1077622728, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C7 caravel_azadi_soc 7976×1077645456, 21552 240.6 kB5d5e4f2138eba6afdcdac07a4a29f84543f581f864e843594654546cede2812d
D1 caravel_dsc 7576×107760, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D3 caravel_user_project 7576×1077615152, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D4 General_Purpose_Band 7576×1077622728, 32328 236.5 kB6811ae73cde667f6af73d71d3f51c22a9facf663835b5f56531fa6987234dbf5
D5 zero_to_asic 7576×1077630304, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D6 fast_gcd_for_large_i 7576×1077637880, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D7 Columbus 7976×1077645456, 32328 225.3 kBa776051e0e365f9465c18ef30a57e045669692787551365cd11bdf51d4c82faf
E1 Space_shuttle 7576×107760, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E2 subservient 7576×107767576, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E3 caravel_analog_fulgor 7576×1077615152, 43104 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
E4 bandgap_reference_de 7576×1077622728, 43104 236.5 kBfa816d5273bb87be51f1adbc1ec25fad6cac78f3319b16afaab3110cd3e7d655
E5 renml 7576×1077630304, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E6 io_expander 7576×1077637880, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E7 digital_pll 7976×1077645456, 43104 225.3 kBb680917504fa0940b3da00793e4459622aaea1a2b8260f70137b2d2141acea58
F1 zero_to_asic 7576×111760, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F2 caravel_fpu 7576×111767576, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F3 BrqRV_EB1 7576×1117615152, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F4 hilas 7576×1117622728, 53880 226.5 kB021acda6887ff8653644cf69ba9119f26a5750b0050a35a56feb6ab22d9db4f7
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 224.4 kB15e7e4b33989278798fad1670fb68faa76e32bc761df3701f07d5b59c4fdfdbc
F6 sha1_engine 7576×1117637880, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F7 caravan_openfasoc 7976×1117645456, 53880 280.2 kBa2cb4a91a7a23382aaf369192ca57ce278d7a34b69f9a0188dea132e4fd82ee3

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 HVNTM on mpw-002

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

Mask layer
HVNTM — High Volt. N-tip
Process step
68 — HV N-tip mask formation
Drawn layer
125:20
Derived from drawn layers as
125:20 OR ((65:20 AND 93:44 AND 75:20) NOT 81:2)
Layer on the mask-layer GDS
39:0
Detail
drawn hvntm merged with a created part (ndiff AND hvi, outside areaid.ce); only the drawn part is rendered
Used in SKY130
yes

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.