mpw-007 / HVNTM High Volt. N-tip

the HVNTM shapes of all of mpw-007

40 of 40 dies rendered.

576,826 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 258.3 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_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-007_HVNTM_reticle.png
the stitched reticle, full resolution
5.3 MB 14bed177858208c829cdb5b767e6dc81d21ca857d7a8b612e6c5e3fdfb5a4fd0401f0761 be81016d19fc9c98181562260bc7c9a6e22ff37ce2b0eceb9ba54e8aa7d14204
mpw-007_HVNTM_reticle_overview.png
the same image scaled to fit 4096 px
384.7 kB b21ec96f55bea6b475efbacfb7694f62c30efb22c29a36737f0b92db85811b8e3d024212 9cfc46a61e9144d464dc4cc5779e5d15abc873a9a165ed25bbce0ff85199dd85
mpw-007_HVNTM_tiles.json
where every tile belongs, and its checksum
13.6 kB 2e097c64b475b0e6d2f4ae6fbdd83953c7313c9aaf85c0c4fdd3fe695c24893e7e0b19cf dadcfa7cc5d9990f8adb76c68e5988e4eee27588f0025296f1bd06d1cec41826
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, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A3 microwatt_mpw7 7576×1077615152, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A4 yonga-can_controller 7576×1077622728, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A5 rocketalpha 7576×1077630304, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A6 riscduino-dcore_d3_3 7576×1077637880, 0 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B2 riscduino-score-s4-3 7576×107767576, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 221.1 kBa0ddae35ecdcbe0fb37795f006747029c76a3527927c23e33f00941057dce063
B4 sramtestvehicleg3 7576×1077622728, 10776 220.5 kB6564136bc42252d320c06c7dea7b991bf156f3caf4b08bb5c341cfef8c85ca9c
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 221.3 kBeafdc68f19b49017c43a74b4fc853badd161574765190b823b9662780254f117
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
B7 reram_crossbar 7976×1077645456, 10776 245.9 kB0e6e22a50ed4bc73f8a1a0466f37d62204c6c8033f7b8fba7cab5bdea6ebce63
C1 trainable-nn 7576×107760, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 220.5 kB06069681560eac4ec7ace06dfea01f1307704b6267b26b965e7c26750a7249ea
C3 bitcoin_mining_asics 7576×1077615152, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C4 yonga-mcu 7576×1077622728, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 240.6 kB5d5e4f2138eba6afdcdac07a4a29f84543f581f864e843594654546cede2812d
D1 mixed_signal_circuits-jun13 7576×107760, 32328 220.6 kBa42675b3d5ee3ead9c4e162faf51465dbdd718d01ba7911ca4b9fd9a69c6a7d9
D2 fpga_programming_management_unit 7576×107767576, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D3 warp-v 7576×1077615152, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D4 leaf__mpw7_ 7576×1077622728, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D5 in_memory_computing_sram 7576×1077630304, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 258.3 kB07a2fde700c30dafabe85b179646f360cb387fb35cc4a323d2e83d3970490f13
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E2 in_memory_computing_rram 7576×107767576, 43104 249.1 kB45955716ecde6741411e6371abff12d7e8e26137f6cc944c3b500586a2be8a5c
E3 rram_imc_v2_flat 7576×1077615152, 43104 249.1 kB45955716ecde6741411e6371abff12d7e8e26137f6cc944c3b500586a2be8a5c
E4 topmetalse-dps 7576×1077622728, 43104 220.4 kB25b450c5809cba5601c3e905b407e822fbeb7ceb7b2a22855788e2a2a1dac4cd
E5 chaos_automaton 7576×1077630304, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E6 hehecore_resubmission 7576×1077637880, 43104 236.3 kBfd20ce50fa28a6449f0206c937431f5b7ea7532e382faae1f442969801e94bc2
E7 waveform_generator 7976×1077645456, 43104 240.6 kB5d5e4f2138eba6afdcdac07a4a29f84543f581f864e843594654546cede2812d
F1 rift2core 7576×111760, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F2 rift2go_2300 7576×111767576, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F3 sloci_resubmission 7576×1117615152, 53880 224.4 kBb5a40a09840c3da7cbfe0e811756d8cc205e7ef76cf40c84a447f4d01377d6f7
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F5 crypto_aes128 7576×1117630304, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F6 graphics_controller 7576×1117637880, 53880 240.3 kBfd122d7fc0a470a5216c3e2ea1c01c07cc64fd2d27dcb6d60450bc1c0747a617
F7 prga-test 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-007

Resolved: reticle 5CS8016AC, run mpw-007, 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-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.