mpw-007 / LVOM Low Voltage Oxide

the LVOM shapes of all of mpw-007

40 of 40 dies rendered.

2,135,118 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 (5.6 MB in total, largest 164.4 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_LVOM_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_LVOM_reticle.png
the stitched reticle, full resolution
4.1 MB 4cc1ab3dad751876b09ed393618dd6bb44ac083c401e68d3cf16a97f4d652d5022dda0b9 d1f512144cb00cb67f5622fb8f5162f47f86e81e1b048703baf3b6162052b795
mpw-007_LVOM_reticle_overview.png
the same image scaled to fit 4096 px
233.2 kB a54d7719b33efcd4a47141665df73291189c28ab67fc197bcc0e3cee0184ad3a13ee54b6 d5b554e165695367aaaabadb5d2c20059502119ba5b990a59e47e8e1f7541076
mpw-007_LVOM_tiles.json
where every tile belongs, and its checksum
13.5 kB e097787dda04fa63a7bc30514f57b099735a26491118162f80259efdee12283c14024dd4 17aa5627c19a26764a0eebeec71ee53aae8d3476f5ce45e8aa1ba93a851d49d4
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, 0 131.8 kBdfc67b9d7ef557fa8b2271baf9380f6d5b4f6efd555ea25e41146e8b95dbcb99
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 132.6 kB20e21178538ae098b9038ef87bb0e337c03305ba83b27a38b3f982afec902ce3
A3 microwatt_mpw7 7576×1077615152, 0 132.6 kB8c9505d01a14e79d4f166bf27d9578f7a53001d9d75882b1d70c7d5949a3c667
A4 yonga-can_controller 7576×1077622728, 0 132.2 kB68ac89cfb9e84409d190498da423d00adde19d6f07d3132af7514b9ccd48528a
A5 rocketalpha 7576×1077630304, 0 132.6 kB8e18ac176b9f05b9a097fa882056a0c3172b87194e52815d27b7ef0b95292565
A6 riscduino-dcore_d3_3 7576×1077637880, 0 132.9 kB378652bb6894e173c83ff255529216ba3d20272a9ebe78fdd74da3b8402fee37
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 132.6 kB8c9505d01a14e79d4f166bf27d9578f7a53001d9d75882b1d70c7d5949a3c667
B2 riscduino-score-s4-3 7576×107767576, 10776 132.9 kB378652bb6894e173c83ff255529216ba3d20272a9ebe78fdd74da3b8402fee37
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 129.1 kBd4cceec69ed61cca4c738d564c96145cc51f1eed49f728b81c00131a92ca0ee3
B4 sramtestvehicleg3 7576×1077622728, 10776 128.4 kBb6aea90eb1c68191bb9e0718217d8ffcd29175060801f055661c66452e211fa8
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 128.6 kBaa0a0fcd0c6d37aee45ff1c7a549d4e61ac7de7a837d5eba3d834145df34cceb
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 132.6 kB20e21178538ae098b9038ef87bb0e337c03305ba83b27a38b3f982afec902ce3
B7 reram_crossbar 7976×1077645456, 10776 164.4 kBdba74506ddb625e9d571c40335161c28a962ed1ab736520a0c29b745b21a74fa
C1 trainable-nn 7576×107760, 21552 132.6 kB20e21178538ae098b9038ef87bb0e337c03305ba83b27a38b3f982afec902ce3
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 128.4 kBb60a3fac20a3a3aac48c7754dec713fc9695567b33a860408e5a52b839be64a6
C3 bitcoin_mining_asics 7576×1077615152, 21552 132.6 kB8c9505d01a14e79d4f166bf27d9578f7a53001d9d75882b1d70c7d5949a3c667
C4 yonga-mcu 7576×1077622728, 21552 132.6 kB20e21178538ae098b9038ef87bb0e337c03305ba83b27a38b3f982afec902ce3
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 132.6 kB20e21178538ae098b9038ef87bb0e337c03305ba83b27a38b3f982afec902ce3
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 136.0 kB94795d47db88c3612acd867c5da77cea1fddfe1fbc7bec03a8d0e633286bf9d8
D1 mixed_signal_circuits-jun13 7576×107760, 32328 128.3 kBb72667363c33a8d3aaf3d16bdeb18afd7c8d46938fef33191d1348f2e63903ca
D2 fpga_programming_management_unit 7576×107767576, 32328 132.6 kB20e21178538ae098b9038ef87bb0e337c03305ba83b27a38b3f982afec902ce3
D3 warp-v 7576×1077615152, 32328 132.2 kB8bf185a6a63c0ccbac32745570be01221acb8d125a20426bf8d974cc144e6a1f
D4 leaf__mpw7_ 7576×1077622728, 32328 132.6 kB8c9505d01a14e79d4f166bf27d9578f7a53001d9d75882b1d70c7d5949a3c667
D5 in_memory_computing_sram 7576×1077630304, 32328 132.2 kBaff9c7388778b52ffca7cf821e557827065a3af1df23c865c15314a4ce0131a4
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 132.6 kB20e21178538ae098b9038ef87bb0e337c03305ba83b27a38b3f982afec902ce3
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 137.9 kB7630385847f7b2f762b02930bca6caba531924abc5bc4aa83cc0e24d391a756c
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 132.6 kBa48189f5fee0a8743d523f096ee7632d63de3289b73584604a56658fa66b9fa3
E2 in_memory_computing_rram 7576×107767576, 43104 133.4 kBa7cf36f4474d41112a94a871702291a94378b30ce063f7ef646c27d0c16fa9b6
E3 rram_imc_v2_flat 7576×1077615152, 43104 133.4 kBa7cf36f4474d41112a94a871702291a94378b30ce063f7ef646c27d0c16fa9b6
E4 topmetalse-dps 7576×1077622728, 43104 128.3 kB73a8748ad758179ddd546ac0efa9cc68e7e57fa6b4ee92d4966d09066436d8dc
E5 chaos_automaton 7576×1077630304, 43104 132.6 kBa48189f5fee0a8743d523f096ee7632d63de3289b73584604a56658fa66b9fa3
E6 hehecore_resubmission 7576×1077637880, 43104 132.6 kB8c9505d01a14e79d4f166bf27d9578f7a53001d9d75882b1d70c7d5949a3c667
E7 waveform_generator 7976×1077645456, 43104 136.0 kB05eda63828d55b36a40d59bc45e04334c78e027c2908b5e27c06cc6f674cf8a4
F1 rift2core 7576×111760, 53880 136.1 kB9daaf3dd3ce5d4eb13f1da17f35f2afe769912e044841fb37bbba0301be4226e
F2 rift2go_2300 7576×111767576, 53880 135.7 kBc6f51957b47bdb43f9f71126d66a6c9892b5c9554f7c7d1cff51df3020c320b3
F3 sloci_resubmission 7576×1117615152, 53880 132.3 kB53f7c3bf7c53967905d622fba2d6313a96b799298d59d7a9f4e438b1510dbaa5
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 136.5 kB49748b333de114b81f993aea3bd08a45bfdc7c14475cfc5d5a6a6365bd2599cd
F5 crypto_aes128 7576×1117630304, 53880 136.5 kBaa1037229d8bbf479a69e423bdecc55b0b5d8720599bebd05f491f52576ae54b
F6 graphics_controller 7576×1117637880, 53880 136.5 kBaa1037229d8bbf479a69e423bdecc55b0b5d8720599bebd05f491f52576ae54b
F7 prga-test 7976×1117645456, 53880 140.3 kBa09c2da32fedcd76844eefb18260a9040a583350eb0f65264c9699a74c22b4b1

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 LVOM on mpw-007

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

Mask layer
LVOM — Low Voltage Oxide
Process step
44 — Low voltage oxide mask
Drawn layers
75:20, 80:20
Layer on the mask-layer GDS
46:0
Detail
LVOM = hvi OR tunm and the SKY130 layer sheet
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.