mpw-008 / HVNTM High Volt. N-tip

the HVNTM shapes of all of mpw-008

40 of 40 dies rendered.

593,516 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 (9.7 MB in total, largest 282.8 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_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-008_HVNTM_reticle.png
the stitched reticle, full resolution
5.4 MB 77e2481c292a9d9db9eafade9d0c9da59b985faa4b998a5134d630120cf964de5520c476 2364dd95a4d14c65ae2d4ef66b1d059f09e51d7c33b6a16a2b0072fb4dd92a81
mpw-008_HVNTM_reticle_overview.png
the same image scaled to fit 4096 px
393.6 kB 2b9346f9bc96361a59dde28dbfaa2d2be18002a2854391960c88d9bf8b44ff0dfb79c1a0 dd653bffb7e9ad6e0d871788c3e68f0a85088bcb63acb63e53ea9a6b3ff730fe
mpw-008_HVNTM_tiles.json
where every tile belongs, and its checksum
13.5 kB ddd799b2ac227517eeed156b875b87c2904169447c55089e0a9e2d0edc074ffade424e55 69e7d5b024694c1414ad6115462250efeb16938ed7f028f9a398702a7cbbc81b
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, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A2 iit_indore_mm 7576×107767576, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A4 riscduino-dcore_d4_ 7576×1077622728, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A5 riscduino-qcore_q3_ 7576×1077630304, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A6 caravel_mpw8 7576×1077637880, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 238.4 kBe7d73e71f6ce2ac38052f580fa931eca709ebe73bb449b71f5ac46ff54404cf3
B2 sramtestvehicleg4 7576×107767576, 10776 220.5 kB6564136bc42252d320c06c7dea7b991bf156f3caf4b08bb5c341cfef8c85ca9c
B3 reram_crossbar_rerun 7576×1077615152, 10776 241.6 kB866db50e204b9f0661dbdcebcd1ca0f8203d3f2c4c8ec8b93a926ff7153e8b97
B4 tinytapeout_skullart 7576×1077622728, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B5 soomrv__mpw-8_ 7576×1077630304, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B6 clock_divide_selector 7576×1077637880, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B7 reram_module_mpw8 7976×1077645456, 10776 258.3 kB07a2fde700c30dafabe85b179646f360cb387fb35cc4a323d2e83d3970490f13
C1 shapingthewaves_8 7576×107760, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C2 leosoc 7576×107767576, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C3 decrypt_aes128 7576×1077615152, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C4 approximate_adder_axha 7576×1077622728, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 225.1 kB5dbe05b642a164ce8ead8d3bc93c87430bc64314fd362f32f7ce8c3009fcfe22
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D2 hyperram_controller_for_sky130 7576×107767576, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D3 hardened_alu 7576×1077615152, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 220.6 kBa42675b3d5ee3ead9c4e162faf51465dbdd718d01ba7911ca4b9fd9a69c6a7d9
D6 gf180_vs_sky130 7576×1077637880, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 240.6 kB5d5e4f2138eba6afdcdac07a4a29f84543f581f864e843594654546cede2812d
E1 iiitb_sd_mpw8 7576×107760, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E2 iiitb_sfifo 7576×107767576, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E3 iiitb_alu 7576×1077615152, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E4 ppcpu2 7576×1077622728, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E5 mega4_reram 7576×1077630304, 43104 282.8 kB167b1e01ae680da5203f241bafc64bdd886cb82a7ce6a168e2837bcc58a7e8b8
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E7 sky130cla 7976×1077645456, 43104 240.6 kB5d5e4f2138eba6afdcdac07a4a29f84543f581f864e843594654546cede2812d
F1 uetrv_esoc_v2 7576×111760, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F2 sequencedetection1010 7576×111767576, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F3 testasynctrimux-mpw8 7576×1117615152, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F6 arbiter_puf 7576×1117637880, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F7 unigate-sky 7976×1117645456, 53880 244.7 kB6568eda406f0ddf8e8670bca395669760c37ec349951e361c2004d6ced55c03d

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-008

Resolved: reticle 5CS8017AC, run mpw-008, 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-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.