mpw-007 / PSDM P+ Implant

the PSDM shapes of all of mpw-007

40 of 40 dies rendered.

43,047,889 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 (96.2 MB in total, largest 5.2 MB), 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_PSDM_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_PSDM_reticle.png
the stitched reticle, full resolution
81.2 MB 9a151b81abfa4de736249e844285acefec8da456c1d622643c65906bdbe55afdb55ec531 fd40901734ccb08d755c7547fc632d36695f96059a4962204dce2ab147b91811
mpw-007_PSDM_reticle_overview.png
the same image scaled to fit 4096 px
3.0 MB f871a7c913ac86d3ab33bebfbb4f0ca1e5dc8a1ec4e5ece30a1effef5d37670ba456d2c0 65dcbdefd84208622b3b56de097bcbdef2d8d5ac0a81a55f4d67c7d6403622b8
mpw-007_PSDM_tiles.json
where every tile belongs, and its checksum
13.6 kB 125b2a540df3ae836665b044d94f33e24ae2443c25d6537912251b7c87ff1940fcae672f d794b979005f9bba5df6ec60c7273daafadd882bd4f096a5460821ce41d90c46
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 1.8 MBc2948d0ddc29247b5be8d3be714cc68b6b18112627e356b449886fd3a4384d48
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 2.4 MBce35150019553da1dc7d72d931c60fae703bf36f9d2f103c6d55679f8690664a
A3 microwatt_mpw7 7576×1077615152, 0 3.7 MB4aedc01108fd7c02319c9310193c9ee69984ef0fa5dedbe996cd614b39e62b8f
A4 yonga-can_controller 7576×1077622728, 0 1.5 MB865d46db692db3e7f8b8c6b290fcff163e77140f85e9563e1e09e242fe7fd57c
A5 rocketalpha 7576×1077630304, 0 3.7 MB07c20fac88f4dc3a041701c27921d9cb2a5a24c390749335a8c73f0a0859c2a2
A6 riscduino-dcore_d3_3 7576×1077637880, 0 4.1 MB2dcfca3962b801b17f0d02bf231195827f0a1ee4936b66e7a2bd0d72b9062a93
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 4.1 MB14070c068f3ccf764fac3f688c662a47de7a6d8f554143de6fb60dac2e7410ad
B2 riscduino-score-s4-3 7576×107767576, 10776 3.8 MB18824cdb8c59b89415b4b16ffead98c0a9419423345977e23746f96c9a91a7f6
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 1.1 MB2b148ef80e3b66915bd1314f20f4b699153e624f034d4bf35905a96f1046a6ae
B4 sramtestvehicleg3 7576×1077622728, 10776 1.4 MB838ffe7722b7ceb0ad511011c18bf609fa7045fc892940ccfef2d12b261697ee
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 1.4 MBaba16344740eabc2b979cb7b63bf630eabcad37bac9bee6c625581d4fe44b559
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 1.5 MB4248017830806f930ff6f974ec2b6042e2adf6ed8344e55882a9370a9b6b3c23
B7 reram_crossbar 7976×1077645456, 10776 1.5 MB7c9d1419c7ae275f8916ca3ad7249f1af814781e25d7626de922c39ed6fa2045
C1 trainable-nn 7576×107760, 21552 2.8 MB1692f48b79757adbd71133aea88fd0b954440eabd6af006658b8729ca031c33b
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 1.4 MB48757e77d86a0a82d01a36f296aca7c5e3bd5301c5644d353438aa866939f11c
C3 bitcoin_mining_asics 7576×1077615152, 21552 1.6 MBdd39ab325ef65a1aa6c0db0af6fd7370a71809a07f0b0cca7638168ff7892177
C4 yonga-mcu 7576×1077622728, 21552 3.0 MBb69a4cba6b8e4cbaaf44cdd754425032dc54c252082b67d36f21852e00d232f4
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 5.2 MB1dab808107bed756067b1c7ee589bc228cbef4ffea2f1f8a5a14dd4f318a693a
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 3.5 MB8c2f1c9258438e82847ee0adcf1699a3abdb50bdf10d8dbe70c5d96e3f7ea628
D1 mixed_signal_circuits-jun13 7576×107760, 32328 1.3 MB396b4594b0b121b769f4c46c327760145f0d9cc80558d35d0b471f879ee35973
D2 fpga_programming_management_unit 7576×107767576, 32328 2.7 MB478900db2d49ef27189bcbc40cc80dbccc8b4f09ca3f0be612dc71ad15addc73
D3 warp-v 7576×1077615152, 32328 1.8 MB8ea7f8d78be343ee8e1afe9b8e7d2f4cb886864338553b44eab24e16da56390e
D4 leaf__mpw7_ 7576×1077622728, 32328 2.2 MB78ee2d77777b371e5d9ec4f66e87b1087f217a3f31883be655b684125617a19e
D5 in_memory_computing_sram 7576×1077630304, 32328 1.7 MB8cf879867a5a216b5b4a5f9c81aa68f2a0f0221ed64c31bc10153d05bf4a482c
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 1.4 MB28ea735b5aee7e7cf372bc0df5abd6ac9c4d8e506ab7997f9dfc6e24b8815ffa
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 1.4 MBd12531b708601949022e12dd15690ac5ec8140f50de20b97a41241367b8955a9
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 1.5 MB2c1fb70cac336d267f6d311ee16ac542e7dac6657312a67171e82430e20f17a8
E2 in_memory_computing_rram 7576×107767576, 43104 1.7 MBe7d408a1051b9a14d45c45712f51c2bf72d1c04e3fa2466f8fbde358841daeb6
E3 rram_imc_v2_flat 7576×1077615152, 43104 1.7 MBe7d408a1051b9a14d45c45712f51c2bf72d1c04e3fa2466f8fbde358841daeb6
E4 topmetalse-dps 7576×1077622728, 43104 1.4 MBa7369561f872cab8cd5992761bde2cc8131542a1f2ed96d66319a1bf539e2721
E5 chaos_automaton 7576×1077630304, 43104 5.0 MB8dd29925caf99f4f4b5f87538571aa2e4861c6f1ef776b59d4462a8cc9a84096
E6 hehecore_resubmission 7576×1077637880, 43104 3.0 MBc3d67cee52f530d6618b7d23ea9ad5ebcc302fb164590f4c5cbc961d409db056
E7 waveform_generator 7976×1077645456, 43104 1.7 MBdc093489a8b62191336d3aade6d76d37d2b2595bf68af7a93f34b276e60781ff
F1 rift2core 7576×111760, 53880 1.8 MB27fd7180db8ae36bd761b2f231263fb13bc527bf8468832d4af075f8a895439f
F2 rift2go_2300 7576×111767576, 53880 4.8 MB18bba019f91e62575741aff0514851aac3a05a562ac76db9f611d55e492f20a5
F3 sloci_resubmission 7576×1117615152, 53880 1.4 MBc97243482feb04232600d72d4bb2765ec583510abc0dae996fc8c0beb97ed3af
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 1.4 MBaada3968732cc475df29ae05ca22423fa1c66d852cfcb0aa1bdaca7d3940b649
F5 crypto_aes128 7576×1117630304, 53880 2.3 MBa84ddb699b54849e00921bb56c47048c3b79daf6d95780dd8aed5e8d0b4f8fe4
F6 graphics_controller 7576×1117637880, 53880 2.4 MBfbb5fd3d382c79ea177294a55b9d2b9941bddd47a4a2135675747cd935586389
F7 prga-test 7976×1117645456, 53880 3.9 MB2b6ab1f4edd185b54d9b278e69e97de187fe49511926a7bb1d495d3d4cca3c21

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 PSDM on mpw-007

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

Mask layer
PSDM — P+ Implant
Process step
81 — P+ source drain implant mask
Drawn layer
94:20
Layer on the mask-layer GDS
32:0
Detail
PSDM = psdm OR grown psdm over pnp emitters
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.