mpw-007 / LVTNM Low Vt Nch*

the LVTNM shapes of all of mpw-007

40 of 40 dies rendered.

158,167 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 (7.5 MB in total, largest 265.2 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_LVTNM_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_LVTNM_reticle.png
the stitched reticle, full resolution
5.5 MB 504ecb353ce82c4dd556574526ce973287563a6ed3910f31cac791a9e6321dc444ace49a 64c7c2a10e1580121dc06f8b88b01169a25b09f4186ad8c003aeed2c8d19820b
mpw-007_LVTNM_reticle_overview.png
the same image scaled to fit 4096 px
478.8 kB cc72b8d74b1ca4e11ed085a82a48b9a357467422fae14988f64e4391b8032b1fc0b7b0a5 3843e440a581f3e722542e0b497612e514bc5eb1c4ad22a5fb2be3993c10aa5b
mpw-007_LVTNM_tiles.json
where every tile belongs, and its checksum
13.6 kB 973536fb43bc1080efc1c2c3ded6ffc55e561b1143df136e37dde48aba2bb55ca02814d1 2cb90bd14c52bdfb375483d5aa5d077ab9b0a0a31520f5e59a7bc551dbcb5673
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, 0 202.2 kBc805976ad169e90084e3ad39c8b1219aeae14e34b448e018e1e007f1a25d3991
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 178.9 kB7f07ffbea70ae69b2f8f480658cc24f40106ee0edf5c17af0094e93996e5907c
A3 microwatt_mpw7 7576×1077615152, 0 201.8 kB488009d43e613bcc1deaf262810b823fe62eeec18845e35576e8620e9a14be4d
A4 yonga-can_controller 7576×1077622728, 0 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
A5 rocketalpha 7576×1077630304, 0 198.3 kBb69c196a4a262afae55765d9930ffeebaaf923744c062385cfffd53803b84d07
A6 riscduino-dcore_d3_3 7576×1077637880, 0 200.1 kBfba9ae2386f481f6642a168d53b769b555ade5736725b4cce68ec7a45ecb30cd
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 205.8 kB38415375917b765230b360f11571a9a1b081aec35fc74d799ff91ee746085a87
B2 riscduino-score-s4-3 7576×107767576, 10776 197.3 kB544f949777b5977d391871b472c243a094804a34087f04d4c66a89a6073844cd
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 154.4 kBbfd3f6e5f04cd1a4c492b6a77f6ebaa8744a86886eb05043e54290f1d7229235
B4 sramtestvehicleg3 7576×1077622728, 10776 159.1 kBc86515aea253664138907268dc7f0704c51bb024eb6e288bad9b17d683c9557f
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 161.0 kBd60b39c5dd2aa272bdd11b0d55a64beff45178d51b5284ba2a9ba916a39ca006
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 166.6 kB6f4d3ac18f4d7f91bf38629d045be20a94b7c80058503b2d6a23ae1f96d68c32
B7 reram_crossbar 7976×1077645456, 10776 171.8 kB1e7d4b3b6c28f61796d3c65f1f9bad94b98524301eaf54fa59ca015bb91a414c
C1 trainable-nn 7576×107760, 21552 172.4 kBff69efbca5b0040117ba98df430db8a5d7722a0fb9a14868c4b19b95ebd84d9f
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 165.4 kB6996540e01311327bc00bb9fc1bd232ae89cda77191e94bc0354ecf6fcf9b7c0
C3 bitcoin_mining_asics 7576×1077615152, 21552 167.0 kB17ad618fcef3548cff8495af3f38e5228da12568bdf47ecb426179417db5cc7b
C4 yonga-mcu 7576×1077622728, 21552 181.0 kB030b2a86634dd21f135a7a4a9d52c83cb1d084db0afd0729737aea3db05ca703
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 190.1 kBf601b14b9998de26d3852740e30235726c945058de6bf5b48d44d375e0c987e9
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 202.1 kB4f62ea738d9911f96a0f5c004373ebbc014943dcf292a2047378ad529453688a
D1 mixed_signal_circuits-jun13 7576×107760, 32328 159.0 kB5030fe7b0ed99a84788adb9499267b63a251b83ee444cf7953b50fbdd446bfaa
D2 fpga_programming_management_unit 7576×107767576, 32328 213.4 kB3afe50fc34a9138e3516d6fe309542a1a6e2c40d91e2dfc8aefb6ce44e41a28f
D3 warp-v 7576×1077615152, 32328 169.3 kB13874f26dcf725d64619c28fe64490f33943ca83dbc085ccb97df94b8c76eba0
D4 leaf__mpw7_ 7576×1077622728, 32328 170.1 kB1dd52526480a5ace87384d7496a159bb047db5ac4ecc5aa892cef59884b14037
D5 in_memory_computing_sram 7576×1077630304, 32328 169.6 kB3e665c7242b8728f4e9c75b12dec3511421bb0654ee234ec2832b7b68e75c521
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 167.6 kB8a6bcb14fb853379d2c603b4cddcf973f83e8dac15fa3b4ea561a7f0b8b799a2
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 162.7 kB1424ddd81dacdfbaabe9805e29aff9d2d6c767e72450013e9d6d3c81e3905dc3
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 166.7 kBd6b3d5ff92819a82074dde4d1e34c8c0dd8378de9a808a64a93431119a2bfabf
E2 in_memory_computing_rram 7576×107767576, 43104 170.4 kB9c2ead1423849762268bb35fad1c75ccd45cc22b27407c86b343990720c5e58a
E3 rram_imc_v2_flat 7576×1077615152, 43104 170.4 kB9c2ead1423849762268bb35fad1c75ccd45cc22b27407c86b343990720c5e58a
E4 topmetalse-dps 7576×1077622728, 43104 161.7 kB12e786142b2bcfd55f9c637407971b693132fd3a0a522ad8f153d0d0037b7cb0
E5 chaos_automaton 7576×1077630304, 43104 241.5 kBbf5147f0c73775edd128999797da31aa851f4cc56d21255ff4033d29e1f1eddc
E6 hehecore_resubmission 7576×1077637880, 43104 206.5 kBe8a1830325dd09fb1656e7124637e439c0f149ad8de7f1c8aa6352419dc33080
E7 waveform_generator 7976×1077645456, 43104 173.2 kB44b4b8ec50cde35e001f9be5994c4fd710811e74a35b3ab0b2900bff54885ac0
F1 rift2core 7576×111760, 53880 173.6 kB42610f6458b63518ccd2201ac26cc00cdaf004a9b2c70f0839b94457e4ed57e0
F2 rift2go_2300 7576×111767576, 53880 179.2 kBab6acd113937758ef0727ab99023a56f9a9a7b3d3b955d29081d9a1c6a5144a8
F3 sloci_resubmission 7576×1117615152, 53880 173.1 kB0a34c164e9096a501f32b80fc530264b03d219e005d91c055ae2f3f92e785609
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 170.5 kBe122aff8e12d91153c9512a73fc248c1892c1bd0d17192ce9cab6c629cade39d
F5 crypto_aes128 7576×1117630304, 53880 174.6 kB26c64449d8213ca76fda0dd567a31827b148cf7d4796237e5336211ce6d5f374
F6 graphics_controller 7576×1117637880, 53880 184.8 kBdba471427c0a0b674602ff9f4c8d6f33aa8f3e7adc95ccd50ce5381ef84c1d22
F7 prga-test 7976×1117645456, 53880 265.2 kBda9190ac89a3ae7aa33a5c0ec1d9870eba98c4fa9f04cb01bb7f8dab5d779939

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 LVTNM on mpw-007

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

Mask layer
LVTNM — Low Vt Nch*
Process step
14 — Low Vt NOMOS mask
Drawn layer
125:44
Derived from drawn layers as
125:44 OR (64:20 AND (78:44 OR 81:2))
Layer on the mask-layer GDS
25:0
Detail
drawn lvtn plus a created part [nwell AND (hvtp OR areaid.ce)] OR (LV nwell over varactors); 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.