mpw-005 / LVOM Low Voltage Oxide

the LVOM shapes of all of mpw-005

40 of 40 dies rendered.

1,996,759 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 (5.5 MB in total, largest 144.5 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-005_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-005_LVOM_reticle.png
the stitched reticle, full resolution
4.1 MB 647005e8592e04f2818ffedba05bb39b253903df54a176e16b00817a004c0a367edadebe 5adeb81b587359d4fd96372974123cbbaa5d69c02cab51fb515bfbbc5d9fa744
mpw-005_LVOM_reticle_overview.png
the same image scaled to fit 4096 px
219.8 kB ba5b4741df0ac01a8d3152f03efad68693c1ce0cd81ffed701e6937ef9c4426f877142cd 23411017cf85049123c63a0d2e7d15a0a2aafa0517418dd3c0e107b87f5e88b4
mpw-005_LVOM_tiles.json
where every tile belongs, and its checksum
13.5 kB af23d0f84039b77f52246141343f0f15f121f30b11f1ec077f23586e94fd458f80052e4f 7bdd02b9e404248b70a1943e7dbdad640d4d1fa4c656abb473d1ba3eaeb38e65
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 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 128.3 kB9cea1018f983db31a5defc882e1a8fa69ec31c04e2a32ed03355ca46fe78b828
A2 mpw5_cache 7576×107767576, 0 132.1 kB842b1b5c8a022a0693e5339dd00add53eed322b57c2c8f0a113096c332e2bf91
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 132.2 kB298cfa4f36fd5098591880570d4dad047d15c1dbbc81a037e0e87c37ba2584ab
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 131.5 kB7bab3181965e08affab7e335f4ccd868fa03ba2fdbb36281eb10c11d2016a110
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 132.2 kB298cfa4f36fd5098591880570d4dad047d15c1dbbc81a037e0e87c37ba2584ab
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 132.1 kB842b1b5c8a022a0693e5339dd00add53eed322b57c2c8f0a113096c332e2bf91
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 131.5 kB7bab3181965e08affab7e335f4ccd868fa03ba2fdbb36281eb10c11d2016a110
B2 microwatt_mpw5 7576×107767576, 10776 131.7 kBe47e55b19712e1e5b1377a6b82529bf0afba27c82b67d21d3b7d181ba899bbea
B3 armleo_gpio_mpw5 7576×1077615152, 10776 144.5 kB91d01261183ab75a49d2d486676b959534e6095e74066672af9d6869cc753846
B4 fabulous_efpga 7576×1077622728, 10776 132.1 kB842b1b5c8a022a0693e5339dd00add53eed322b57c2c8f0a113096c332e2bf91
B5 i2c_controller 7576×1077630304, 10776 131.9 kBafeaf4d02f76afced8a2f4aa1f7f12467511468ad66c613097d86deecdbf3c5b
B6 lbist-mbist 7576×1077637880, 10776 131.5 kB7bab3181965e08affab7e335f4ccd868fa03ba2fdbb36281eb10c11d2016a110
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 135.5 kBd7062ec1ec189c0347c9a2571e6b68ad751af9814247e99aa54cbe3a9200133a
C1 riscduino-qcore__q0_ 7576×107760, 21552 131.7 kBe47e55b19712e1e5b1377a6b82529bf0afba27c82b67d21d3b7d181ba899bbea
C2 high_speed_vco 7576×107767576, 21552 128.3 kB9cea1018f983db31a5defc882e1a8fa69ec31c04e2a32ed03355ca46fe78b828
C3 openfasoc-cryo-gen 7576×1077615152, 21552 133.6 kB957d491520e82609cd60f58d9b477d72c3ab53ac89ba2665515b2f1795ad7b60
C4 microwave_signal_generator 7576×1077622728, 21552 128.2 kBe1843b6fd1df1845c753c7c062b36469faee558290709bb21175d5d5c5621724
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 142.7 kB0584d9cacda03b4b3390ccad44c799a51dc8bb69a6388526d2047b1ffec62247
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 131.9 kBa8a76509bf0096ae77c421bf56566c8d07253653eeca0c2088779878cc4853f5
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 128.3 kB40bb5ce93f5672629d10166045003d5738ef887f4d0654ffc1be0d93e6e59419
D2 azadi_iii 7576×107767576, 32328 131.7 kBe47e55b19712e1e5b1377a6b82529bf0afba27c82b67d21d3b7d181ba899bbea
D3 tia_for_physic_experiments 7576×1077615152, 32328 128.1 kBa0ff7e200e95b281bc6d5433de890decb4e1da8592cec9e58288c7b88ee265c7
D4 qf105 7576×1077622728, 32328 131.8 kBec2ac0fd3e07447d9c6d4cab3dfa34b8b593195ff238d0beb11662ce657ac5a2
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 131.7 kBe47e55b19712e1e5b1377a6b82529bf0afba27c82b67d21d3b7d181ba899bbea
D6 junga_soc_mpw5 7576×1077637880, 32328 132.2 kB298cfa4f36fd5098591880570d4dad047d15c1dbbc81a037e0e87c37ba2584ab
D7 nand_flash_mpw-5 7976×1077645456, 32328 135.6 kBad1f9baa9526b5661e2f1db97c95188dc42bc98965929b141867122e292351a8
E1 kasirga-c0-mpw-5 7576×107760, 43104 131.9 kBafeaf4d02f76afced8a2f4aa1f7f12467511468ad66c613097d86deecdbf3c5b
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 132.6 kB8b86ab172cba7029caf9acd09bfbbf273454f28f45359d6833e79cd183cfd374
E3 wirelessenergyharvesting 7576×1077615152, 43104 128.3 kB40bb5ce93f5672629d10166045003d5738ef887f4d0654ffc1be0d93e6e59419
E4 caravel_raster_engine 7576×1077622728, 43104 131.7 kBe47e55b19712e1e5b1377a6b82529bf0afba27c82b67d21d3b7d181ba899bbea
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 128.2 kBd3e1706acb53c393db3d3ba7b5d9d96c9656b1e0cd54d9a1bf631312876e8e8c
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 131.9 kBafeaf4d02f76afced8a2f4aa1f7f12467511468ad66c613097d86deecdbf3c5b
E7 alu 7976×1077645456, 43104 135.9 kBb145488ec1083628e22f6713ebb39a3178973d734360a4d2e0aab2bfa7d0d392
F1 4ft4 7576×111760, 53880 135.6 kBf8fa8f3079393af14a945b1e62983186cfff762afb6ad99d93080860a72260d0
F2 rng_chaos 7576×111767576, 53880 135.6 kBf8fa8f3079393af14a945b1e62983186cfff762afb6ad99d93080860a72260d0
F3 uetrv-ecore 7576×1117615152, 53880 136.0 kBba75ed4a160727f0f76d30c107286635dcfc2f9385374dc79f707dd57f99218d
F4 systolic_array 7576×1117622728, 53880 135.8 kB9d24a11e4e273a328e39a487f935fade6e351856be7ddfa6bb5bbbc2678848d8
F5 reram_test 7576×1117630304, 53880 132.3 kBa5e38a73b3dce2d5b8c024cf3a5521d2052c5b0ec418ebc59e0de0aeaf4a8f2d
F6 secure_memory 7576×1117637880, 53880 135.6 kBf8fa8f3079393af14a945b1e62983186cfff762afb6ad99d93080860a72260d0
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 140.0 kBa95f41dcf42a6f4341b28869ef4fa0280e4b4be41df78adedb5db0872cb0b371

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-005

Resolved: reticle 5CS8011AC, run mpw-005, 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-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/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.