mpw-007 / PDM Pad (scribe protect)

the PDM shapes of all of mpw-007

40 of 40 dies rendered.

4,911 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 (4.8 MB in total, largest 121.8 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-007_PDM_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_PDM_reticle.png
the stitched reticle, full resolution
3.7 MB 4725405064fbf24efa3d26daafe20ea8911ff4c91356cfbb6bf0a392bae86893490adfad f0c2fbe416a728ff66175977916321b59a17f3a52558d78517b2c30f37ba2c03
mpw-007_PDM_reticle_overview.png
the same image scaled to fit 4096 px
82.4 kB cbf5977be34de4789c6642e86d91609bc369813abbee95491b1b82baf4e06a4f452da251 2b17ebcc3a4bcb0011dde4e8f74c151682c48ace08458a79616dbd9c9c2da34d
mpw-007_PDM_tiles.json
where every tile belongs, and its checksum
13.5 kB 1068ddac1b1494b4290be9e9c0254936e4207d6df001da55104a6721327a9d7b134cc07e 9498e3a555096d4ee1449168643c582996b1572039e86e47d63560d3456f424d
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, 0 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
A3 microwatt_mpw7 7576×1077615152, 0 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
A4 yonga-can_controller 7576×1077622728, 0 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
A5 rocketalpha 7576×1077630304, 0 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
A6 riscduino-dcore_d3_3 7576×1077637880, 0 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
B2 riscduino-score-s4-3 7576×107767576, 10776 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 121.8 kBbc38206121683d2201dee09e7cc67396afcd3e7df3b965cdbbdf1b61279fa794
B4 sramtestvehicleg3 7576×1077622728, 10776 112.2 kBb6b1b59b7e20aa8249bc15b3302065eeea15899e2d361689a0258a0a22027ae4
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 112.2 kBb6b1b59b7e20aa8249bc15b3302065eeea15899e2d361689a0258a0a22027ae4
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
B7 reram_crossbar 7976×1077645456, 10776 116.0 kB8511ad9b300cfe6515a8d884f050d1d00916a83b64539b1b7d01c4bb4b071768
C1 trainable-nn 7576×107760, 21552 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 112.2 kBb6b1b59b7e20aa8249bc15b3302065eeea15899e2d361689a0258a0a22027ae4
C3 bitcoin_mining_asics 7576×1077615152, 21552 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
C4 yonga-mcu 7576×1077622728, 21552 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 116.3 kB0b6fc7317dd24588d7de08fcf82c35ec8f3362ec3dddf7d84d8d9d1ec2101a8a
D1 mixed_signal_circuits-jun13 7576×107760, 32328 112.2 kBb6b1b59b7e20aa8249bc15b3302065eeea15899e2d361689a0258a0a22027ae4
D2 fpga_programming_management_unit 7576×107767576, 32328 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
D3 warp-v 7576×1077615152, 32328 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
D4 leaf__mpw7_ 7576×1077622728, 32328 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
D5 in_memory_computing_sram 7576×1077630304, 32328 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 116.0 kB8511ad9b300cfe6515a8d884f050d1d00916a83b64539b1b7d01c4bb4b071768
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
E2 in_memory_computing_rram 7576×107767576, 43104 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
E3 rram_imc_v2_flat 7576×1077615152, 43104 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
E4 topmetalse-dps 7576×1077622728, 43104 112.3 kBeeff9cdbaaa289696d5fba1ce869b5e4b8665fd83d43ff0c0a66f168dd1426fa
E5 chaos_automaton 7576×1077630304, 43104 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
E6 hehecore_resubmission 7576×1077637880, 43104 112.4 kB8b3e83c7e04f25e37ce8731f3e507e85669fdbcd354dc0366c6cdf779748cd98
E7 waveform_generator 7976×1077645456, 43104 116.3 kB0b6fc7317dd24588d7de08fcf82c35ec8f3362ec3dddf7d84d8d9d1ec2101a8a
F1 rift2core 7576×111760, 53880 116.2 kBd10962219cbe861e700172c53f7c02735c4bdafa504055669e433e36532e4e5d
F2 rift2go_2300 7576×111767576, 53880 116.2 kBd10962219cbe861e700172c53f7c02735c4bdafa504055669e433e36532e4e5d
F3 sloci_resubmission 7576×1117615152, 53880 116.0 kB39cc973b528f3895b0fe86d4de77f89d848fb1d8bb93f01d3de2afd566e2bddd
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 116.2 kBd10962219cbe861e700172c53f7c02735c4bdafa504055669e433e36532e4e5d
F5 crypto_aes128 7576×1117630304, 53880 116.2 kBd10962219cbe861e700172c53f7c02735c4bdafa504055669e433e36532e4e5d
F6 graphics_controller 7576×1117637880, 53880 116.2 kBd10962219cbe861e700172c53f7c02735c4bdafa504055669e433e36532e4e5d
F7 prga-test 7976×1117645456, 53880 120.3 kBf969326ccac88723beaad009063417ca0ebce161dbe2e02c20cc92fa4c1e36ff

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 PDM on mpw-007

Resolved: reticle 5CS8016AC, run mpw-007, mask PDM

Mask layer
PDM — Pad (scribe protect)
Process step
168 — Pad mask
Drawn layer
76:20
Layer on the mask-layer GDS
37:0
Variants
Pad (scribe protect), Pad (scribe unprotect)
Detail
PDM = pad OR (cpmm AND areaid.mt) sized by cpdm.4
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.